The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.
The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method identifying a covariant of interest with respect to drug response phenotype (DRP) of a treatment is disclosed.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
3.
MACHINE-LEARNING-ENABLED PREDICTIVE BIOMARKER DISCOVERY AND PATIENT STRATIFICATION USING STANDARD-OF-CARE DATA
The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.
Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 10/00 - Instruments for taking body samples for diagnostic purposesOther methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determinationThroat striking implements
A method for filtering out artifacts from a microscopic image of a tissue includes determining a plurality of frequency values corresponding to a plurality of pixels in the microscopic image of the tissue; grouping the plurality of pixels into a plurality of pixel clusters based on the plurality of frequency values corresponding to the plurality of pixels; identifying, from the plurality of pixel clusters, one or more pixel clusters corresponding to one or more artifacts in the microscopic image; and filtering the microscopic image by removing one or more regions in the microscopic image corresponding to the one or more pixel clusters corresponding to the one or more artifacts.
An exemplary method for determining a sampling protocol for sampling tissue cores for a tissue microarray includes obtaining an initial plurality of tissue cores from an image of a tissue slide; selecting a first subset of the initial plurality of tissue cores based on a first candidate sampling protocol; inputting the first subset of the plurality of tissue cores into a machine learning model; evaluating the first candidate sampling protocol by evaluating a first output of the machine learning model; selecting a second subset of the initial plurality of tissue cores based on a second candidate sampling protocol; inputting the second subset of the plurality of tissue cores into the machine learning model; evaluating the second candidate sampling protocol by evaluating a second output of the machine learning model; and determining the sampling protocol based on the evaluation of the first candidate sampling protocol and the second candidate sampling protocol.
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G01N 33/48 - Biological material, e.g. blood, urineHaemocytometers
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
An exemplary method for determining a sampling protocol for sampling tissue cores for a tissue microarray includes obtaining an initial plurality of tissue cores from an image of a tissue slide; selecting a first subset of the initial plurality of tissue cores based on a first candidate sampling protocol; inputting the first subset of the plurality of tissue cores into a machine learning model; evaluating the first candidate sampling protocol by evaluating a first output of the machine learning model; selecting a second subset of the initial plurality of tissue cores based on a second candidate sampling protocol; inputting the second subset of the plurality of tissue cores into the machine learning model; evaluating the second candidate sampling protocol by evaluating a second output of the machine learning model; and determining the sampling protocol based on the evaluation of the first candidate sampling protocol and the second candidate sampling protocol.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/766 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using regression, e.g. by projecting features on hyperplanes
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
8.
Systems and methods for artifact detection and removal from image data
A method for filtering out artifacts from a microscopic image of a tissue includes determining a plurality of frequency values corresponding to a plurality of pixels in the microscopic image of the tissue; grouping the plurality of pixels into a plurality of pixel clusters based on the plurality of frequency values corresponding to the plurality of pixels; identifying, from the plurality of pixel clusters, one or more pixel clusters corresponding to one or more artifacts in the microscopic image; and filtering the microscopic image by removing one or more regions in the microscopic image corresponding to the one or more pixel clusters corresponding to the one or more artifacts.
Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 10/00 - Instruments for taking body samples for diagnostic purposesOther methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determinationThroat striking implements
The present disclosure relates generally to machine learning techniques, and more specifically to machine learning techniques for generating synthetic spatial omics data based on histopathology image data. An exemplary system for generating synthetic spatial omics images comprises: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving a histopathology image depicting a diseased region of interest of an input tissue sample; and generating a synthetic spatial omics image depicting one or more stained structures of interest within the diseased region of interest by inputting the histopathology image into a generator of a trained generative adversarial network (GAN) model.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
11.
BIOLOGICAL IMAGE TRANSFORMATION USING MACHINE-LEARNING MODELS
Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 10/00 - Instruments for taking body samples for diagnostic purposesOther methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determinationThroat striking implements
The present disclosure relates generally to machine learning techniques, and more specifically to machine learning techniques for generating synthetic spatial omics data based on histopathology image data. An exemplary system for generating synthetic spatial omics images comprises: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving a histopathology image depicting a diseased region of interest of an input tissue sample; and generating a synthetic spatial omics image depicting one or more stained structures of interest within the diseased region of interest by inputting the histopathology image into a generator of a trained generative adversarial network (GAN) model.
Embodiments of the disclosure involve modeling DEL data using factorized molecular representations (e.g., hierarchical mono-synthon and di-synthon building blocks), which capitalizes on the inherent hierarchical structure of these molecules. Using the factorized molecular representations, machine learning models are trained to learn latent binding affinity of compounds for targets and one or more covariates (e.g., load/replicate noise). This leads to improved predictions by the machine learning models in the form of higher enrichment scores, which are well-correlated with compound-target binding affinity.
An exemplary method for predicting one or more adipose depots for a patient includes receiving one or more Dual-energy X-ray Absorptiometry (DEXA) scans comprising at least a portion of a torso of the patient; providing at least one or more portions of the one or more DEXA scans to a trained machine-learning model, wherein the machine-learning model is trained using a training dataset comprising: a plurality of training DEXA scans of a plurality of subjects and a plurality of corresponding Magnetic Resonance Imaging (MRI)-image-based adiposity scores of the plurality of subjects; and predicting the one or more adipose depots for the patient utilizing the trained machine-learning model.
01 - Chemical and biological materials for industrial, scientific and agricultural use
05 - Pharmaceutical, veterinary and sanitary products
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Chemical and biological reagents for scientific research,
drug discovery, and drug development; sequencing reagents;
optical screening reagents; cell imaging reagents;
biochemical assay kits for laboratory use; reagents for
laboratory automation systems and scientific apparatus;
chemical preparations for high-throughput screening;
laboratory chemicals and solutions for research purposes,
excluding those for medical use, including reagents
supporting artificial intelligence-driven workflows and
machine learning applications. Pharmaceutical preparations and substances for human and
veterinary use for the treatment and prevention of diseases;
biological and chemical preparations for medical and
therapeutic use; diagnostic reagents and testing kits for
medical purposes; nucleic acid-based therapeutics, small
molecule drugs, and biologics; pharmaceutical products
informed by predictive modeling and data analysis using
machine learning and artificial intelligence; precision
medicine drug delivery formulations; pharmaceutical drugs;
drugs for medical purposes; medicines for human purposes. Laboratory automation systems and hardware for scientific
research, drug discovery, and drug development; optical
screening devices; cell imaging instruments; DNA sequencing
machines; robotic laboratory systems; biosensors;
high-throughput screening devices; laboratory instruments
for measuring, analyzing, and monitoring biological and
chemical samples; AI-powered hardware and software for
scientific and laboratory applications, including
experimental data generation; devices for sale or licensing
to research organizations and pharmaceutical companies. Scientific and technological research and development
services in the fields of drug discovery, pharmaceutical
development, and biotechnology; providing artificial
intelligence (AI) and machine learning (ML) platforms for
scientific and medical research; development of computer
software and computational tools for drug discovery,
genomics, and data analysis; laboratory research services
using automation, high-throughput experimentation, and data
modeling; software as a service (SaaS) featuring AI-powered
drug discovery, predictive modeling, and biological data
analysis; development of proprietary algorithms for
scientific and medical research; multi-omics data
integration services for therapeutic discovery; consulting
services in computational biology and AI-driven drug
discovery; medical research. Medical consulting services; personalized medicine services,
including diagnostic and therapeutic recommendations;
healthcare services utilizing artificial intelligence for
disease prediction, patient stratification, and management;
drug discovery and development support services from a
medical perspective; medical analysis services for
diagnostic and treatment purposes; providing medical
information and consultancy related to therapeutic research
and development; clinical trial support and patient
stratification services informed by computational biology;
digital health solutions integrating data-driven medical
insights; medical services; health consultancy; healthcare
services; medical treatment services for diseases;
healthcare advisory services; collation of information in
the healthcare sector, namely, the development and collation
of disease knowledge data sets; pharmaceutical advice;
medical clinic services; medical advice for individuals with
diseases; advisory services relating to diseases; advisory
services relating to treatment of diseases; provision of
medicine in the field of diseases, namely, developing
treatments for unmet medical needs.
01 - Chemical and biological materials for industrial, scientific and agricultural use
05 - Pharmaceutical, veterinary and sanitary products
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Chemical and biological reagents for scientific research,
drug discovery, and drug development; sequencing reagents;
optical screening reagents; cell imaging reagents;
biochemical assay kits for laboratory use; reagents for
laboratory automation systems and scientific apparatus;
chemical preparations for high-throughput screening;
laboratory chemicals and solutions for research purposes,
excluding those for medical use, including reagents
supporting artificial intelligence-driven workflows and
machine learning applications. Pharmaceutical preparations and substances for human and
veterinary use for the treatment and prevention of diseases;
biological and chemical preparations for medical and
therapeutic use; diagnostic reagents and testing kits for
medical purposes; nucleic acid-based therapeutics, small
molecule drugs, and biologics; pharmaceutical products
informed by predictive modeling and data analysis using
machine learning and artificial intelligence; precision
medicine drug delivery formulations; pharmaceutical drugs;
drugs for medical purposes; medicines for human purposes. Laboratory automation systems and hardware for scientific
research, drug discovery, and drug development; optical
screening devices; cell imaging instruments; DNA sequencing
machines; robotic laboratory systems; biosensors;
high-throughput screening devices; laboratory instruments
for measuring, analyzing, and monitoring biological and
chemical samples; AI-powered hardware and software for
scientific and laboratory applications, including
experimental data generation; devices for sale or licensing
to research organizations and pharmaceutical companies. Scientific and technological research and development
services in the fields of drug discovery, pharmaceutical
development, and biotechnology; providing artificial
intelligence (AI) and machine learning (ML) platforms for
scientific and medical research; development of computer
software and computational tools for drug discovery,
genomics, and data analysis; laboratory research services
using automation, high-throughput experimentation, and data
modeling; software as a service (SaaS) featuring AI-powered
drug discovery, predictive modeling, and biological data
analysis; development of proprietary algorithms for
scientific and medical research; multi-omics data
integration services for therapeutic discovery; consulting
services in computational biology and AI-driven drug
discovery; medical research. Medical consulting services; personalized medicine services,
including diagnostic and therapeutic recommendations;
healthcare services utilizing artificial intelligence for
disease prediction, patient stratification, and management;
drug discovery and development support services from a
medical perspective; medical analysis services for
diagnostic and treatment purposes; providing medical
information and consultancy related to therapeutic research
and development; clinical trial support and patient
stratification services informed by computational biology;
digital health solutions integrating data-driven medical
insights; medical services; health consultancy; healthcare
services; medical treatment services for diseases;
healthcare advisory services; collation of information in
the healthcare sector, namely, the development and collation
of disease knowledge data sets; pharmaceutical advice;
medical clinic services; medical advice for individuals with
diseases; advisory services relating to diseases; advisory
services relating to treatment of diseases; provision of
medicine in the field of diseases, namely, developing
treatments for unmet medical needs.
17.
COMPOSITIONS AND METHODS FOR TREATING NONALCOHOLIC FATTY LIVER DISEASE
Described are compositions and methods for inhibition of IRS1 gene expression. RNA interference (RNAi) agents for inhibiting the expression of IRS1 gene are described. The IRS1 RNAi agents disclosed herein may be targeted to cells, such as hepatocytes, for example, by using conjugated targeting ligands. Pharmaceutical compositions comprising one or more IRS RNAi agents optionally with one or more additional therapeutics are also described.
Embodiments of the disclosure involve modeling DEL data using factorized molecular representations (e.g., hierarchical mono-synthon and di-synthon building blocks), which capitalizes on the inherent hierarchical structure of these molecules. Using the factorized molecular representations, machine learning models are trained to learn latent binding affinity of compounds for targets and one or more covariates (e.g., load/replicate noise). This leads to improved predictions by the machine learning models in the form of higher enrichment scores, which are well-correlated with compound-target binding affinity.
Disclosed herein are methods for performing in situ sequencing of RNA transcripts with non-uniform 5′ ends. During reverse transcription (RT) of RNA transcripts, RT enzyme is induced to “template-switch” to a separate oligonucleotide provided as the template for the upstream flanking region. This flanking region is grafted onto the beginning of the cDNA, enabling padlock probe detection, rolling circle amplification, and fluorescent in situ sequencing. Overall, the disclosed method for in situ sequencing can be applicable for analyzing exogenously introduced transcripts (e.g., identifying and determining impact of a perturbation including a CRISPR perturbation or shRNA/siRNA/ASO perturbation), analyzing naturally occurring transcripts (e.g., measuring gene expression, detecting splicing events), and analyzing modified, naturally occurring transcripts (e.g., detecting mutations or gene edits).
The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time.
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
21.
IN SITU SEQUENCING OF RNA TRANSCRIPTS WITH NON-UNIFORM 5' ENDS
Disclosed herein are methods for performing in situ sequencing of RNA transcripts with non-uniform 5′ ends. During reverse transcription (RT) of RNA transcripts, RT enzyme is induced to “template-switch” to a separate oligonucleotide provided as the template for the upstream flanking region. This flanking region is grafted onto the beginning of the cDNA, enabling padlock probe detection, rolling circle amplification, and fluorescent in situ sequencing. Overall, the disclosed method for in situ sequencing can be applicable for analyzing exogenously introduced transcripts (e.g., identifying and determining impact of a perturbation including a CRISPR perturbation or shRNA/siRNA/ASO perturbation), analyzing naturally occurring transcripts (e.g., measuring gene expression, detecting splicing events), and analyzing modified, naturally occurring transcripts (e.g., detecting mutations or gene edits).
01 - Chemical and biological materials for industrial, scientific and agricultural use
05 - Pharmaceutical, veterinary and sanitary products
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Chemical and biological reagents for scientific research, drug discovery, and drug development; laboratory chemicals and solutions for research purposes, excluding those for medical use, namely, reagents supporting artificial intelligence-driven workflows and machine learning applications Pharmaceutical preparations and substances for human and veterinary use for the treatment and prevention of diseases, namely, human metabolic diseases, human neurological diseases, human ophthalmological diseases, and human genetic diseases. Laboratory automation systems comprising scientific laboratory research apparatus for screening cells and molecules for understanding disease causes and mechanisms, and recorded software controlling the apparatus, for scientific research, drug discovery, and drug development; Artificial intelligence (AI) and machine learning (ML) platforms, namely, recorded software platforms for experimental data generation for scientific and laboratory applications for scientific and medical research; digital health solutions, namely, downloadable software for integrating data-driven medical insights. Scientific and technological research and development services in the fields of drug discovery, pharmaceutical development, and biotechnology; Platform as a Service featuring artificial intelligence (AI) and machine learning (ML) software platforms for drug discovery, pharmaceutical development, and biotechnology for scientific and medical research; Development of computer software, for drug discovery, genomics, and data analysis; laboratory research services in the field of pharmaceuticals, using automation, high-throughput experimentation, and data modeling software as a service (SaaS) featuring software for AI-powered drug discovery, predictive modeling, and biological data analysis; consulting services in computational biology and AI-driven drug discovery; Medical research services and consulting services in the field of medical research; drug discovery and development support services from a medical perspective, namely, pharmaceutical drug development services; providing medical information and consultancy, namely, providing information and consulting in the field of medical research, related to therapeutic research and development; provision of medicine in the field of diseases, namely, developing treatments for unmet medical needs. Personalized medicine services, namely, making recommendations for medical diagnostic tests and therapeutic recommendations to patients; healthcare services utilizing artificial intelligence for disease prediction, patient stratification, and management; medical analysis services for diagnostic and treatment purposes of patients; healthcare services; medical treatment services for diseases; healthcare advisory services; pharmaceutical advice; medical clinic services; medical advice for individuals with diseases
01 - Chemical and biological materials for industrial, scientific and agricultural use
05 - Pharmaceutical, veterinary and sanitary products
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Chemical and biological reagents for scientific research, drug discovery, and drug development; sequencing reagents; optical screening reagents; cell imaging reagents; biochemical assay kits for laboratory use; reagents for laboratory automation systems and scientific apparatus; chemical preparations for high-throughput screening; laboratory chemicals and solutions for research purposes, excluding those for medical use, including reagents supporting artificial intelligence-driven workflows and machine learning applications. Pharmaceutical preparations and substances for human and veterinary use for the treatment and prevention of diseases; biological and chemical preparations for medical and therapeutic use; diagnostic reagents and testing kits for medical purposes; nucleic acid-based therapeutics, small molecule drugs, and biologics; pharmaceutical products informed by predictive modeling and data analysis using machine learning and artificial intelligence; precision medicine drug delivery formulations; pharmaceutical drugs; drugs for medical purposes; medicines for human purposes. Laboratory automation systems and hardware for scientific research, drug discovery, and drug development; optical screening devices; cell imaging instruments; DNA sequencing machines; robotic laboratory systems; biosensors; high-throughput screening devices; laboratory instruments for measuring, analyzing, and monitoring biological and chemical samples; AI-powered hardware and software for scientific and laboratory applications, including experimental data generation; devices for sale or licensing to research organizations and pharmaceutical companies. Scientific and technological research and development services in the fields of drug discovery, pharmaceutical development, and biotechnology; providing artificial intelligence (AI) and machine learning (ML) platforms for scientific and medical research; development of computer software and computational tools for drug discovery, genomics, and data analysis; laboratory research services using automation, high-throughput experimentation, and data modeling; software as a service (SaaS) featuring AI-powered drug discovery, predictive modeling, and biological data analysis; development of proprietary algorithms for scientific and medical research; multi-omics data integration services for therapeutic discovery; consulting services in computational biology and AI-driven drug discovery. Medical research and consulting services; personalized medicine services, including diagnostic and therapeutic recommendations; healthcare services utilizing artificial intelligence for disease prediction, patient stratification, and management; drug discovery and development support services from a medical perspective; medical analysis services for diagnostic and treatment purposes; providing medical information and consultancy related to therapeutic research and development; clinical trial support and patient stratification services informed by computational biology; digital health solutions integrating data-driven medical insights. medical services; health consultancy; healthcare services; medical treatment services for diseases; healthcare advisory services; collation of information in the healthcare sector, namely, the development and collation of disease knowledge data sets; pharmaceutical advice; medical clinic services; medical advice for individuals with diseases; advisory services relating to diseases; advisory services relating to treatment of diseases; provision of medicine in the field of diseases, namely, developing treatments for unmet medical needs
24.
PREDICTING CELLULAR PLURIPOTENCY USING CONTRAST IMAGES
Embodiments of the disclosure include methods for implementing a predictive model that predicts pluripotency of cells through a cost efficient and non-destructive means. The predictive model analyzes contrast images captured from the cells and outputs predictions of cellular pluripotency at the cellular level. Thus, implementation of the predictive model guides the selection and isolation of cells that are predicted to be pluripotent. Furthermore, the predictive model facilitates retrospective analyses to correlate pluripotency metrics with differentiation success and further enables tracking of cellular pluripotency over time (e.g., to evaluate differentiation of cells).
An exemplary method for predicting one or more adipose depots for a patient includes receiving one or more Dual-energy X-ray Absorptiometry (DEXA) scans comprising at least a portion of a torso of the patient; providing at least one or more portions of the one or more DEXA scans to a trained machine-learning model, wherein the machine-learning model is trained using a training dataset comprising: a plurality of training DEXA scans of a plurality of subjects and a plurality of corresponding Magnetic Resonance Imaging (MRI)-image-based adiposity scores of the plurality of subjects; and predicting the one or more adipose depots for the patient utilizing the trained machine-learning model.
The present disclosure relates generally to providing a cellular time-series imaging, modeling, and analysis platform, and more specifically to acquiring time-series image data and using various machine learning models to model and analyze subcellular particle movements and changes in cellular positional and morphological characteristics using unsupervised embedding generation. The platform can be applied to evaluate various cellular and subcellular processes by generating summary embeddings of time-series image data that enable analysis of dynamic cellular and subcellular processes over time (e.g., the movement of particles within a cell, neurites on developing neurons, etc.) for enhanced identification of differences between cell states (e.g., between sick and healthy cells) and generation of disease models which can be used to analyze the impact of various therapeutic interventions, among other improvements described throughout.
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
Trained machine learning models are deployed to generate predictions of cellular responses to perturbations. A treated representation of a cell is generated within a latent space using one or more disentangled representations, examples of which include a basal state representation of a cell, a learned treatment mask for a perturbation, and/or a treatment representation for the perturbation. Within the latent space, effects of perturbations are modeled as inducing sparse latent offsets. Multiple perturbations can be modeled in the latent space as the sparse latent offsets compose additively (sparse additive mechanism shift). Thus, operating within this latent space enables the modeling of cellular responses to one or more perturbations.
Trained machine learning models are deployed to generate predictions of cellular responses to perturbations. A treated representation of a cell is generated within a latent space using one or more disentangled representations, examples of which include a basal state representation of a cell, a learned treatment mask for a perturbation, and/or a treatment representation for the perturbation. Within the latent space, effects of perturbations are modeled as inducing sparse latent offsets. Multiple perturbations can be modeled in the latent space as the sparse latent offsets compose additively (sparse additive mechanism shift). Thus, operating within this latent space enables the modeling of cellular responses to one or more perturbations.
G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
29.
CELLULAR TIME-SERIES IMAGING, MODELING, AND ANALYSIS SYSTEM
The present disclosure relates generally to providing a cellular time-series imaging, modeling, and analysis platform, and more specifically to acquiring time-series image data and using various machine learning models to model and analyze subcellular particle movements and changes in cellular positional and morphological characteristics using unsupervised embedding generation. The platform can be applied to evaluate various cellular and subcellular processes by generating summary embeddings of time-series image data that enable analysis of dynamic cellular and subcellular processes over time (e.g., the movement of particles within a cell, neurites on developing neurons, etc.) for enhanced identification of differences between cell states (e.g., between sick and healthy cells) and generation of disease models which can be used to analyze the impact of various therapeutic interventions, among other improvements described throughout.
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
30.
CELLULAR TIME-SERIES IMAGING, MODELING, AND ANALYSIS SYSTEM
The present disclosure relates generally to providing a cellular time-series imaging, modeling, and analysis platform, and more specifically to acquiring time-series image data and using various machine learning models to model and analyze subcellular particle movements and changes in cellular positional and morphological characteristics using unsupervised embedding generation. The platform can be applied to evaluate various cellular and subcellular processes by generating summary embeddings of time-series image data that enable analysis of dynamic cellular and subcellular processes over time (e.g., the movement of particles within a cell, neurites on developing neurons, etc.) for enhanced identification of differences between cell states (e.g., between sick and healthy cells) and generation of disease models which can be used to analyze the impact of various therapeutic interventions, among other improvements described throughout.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G06V 10/46 - Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]Salient regional features
G06V 10/62 - Extraction of image or video features relating to a temporal dimension, e.g. time-based feature extractionPattern tracking
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
31.
SYNTHETIC BARCODING OF CELL LINE BACKGROUND GENETICS
Provided herein are methods of pooled screening of cells from different genetic backgrounds. Also provided herein are computer-implemented methods for aligning between a first plurality of images and a second plurality of images of biological samples.
C12Q 1/6881 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
The present disclosure relates to an autonomous system for maintaining and differentiating induced pluripotency cells (iPSCs) based on quality and confluence conditions using machine learning, to obtain differentiated cells for phenotypic analyses and/or other cellular assays.
The present disclosure relates to an autonomous system for maintaining and differentiating induced pluripotency cells (iPSCs) based on quality and confluence conditions using machine learning, to obtain differentiated cells for phenotypic analyses and/or other cellular assays.
The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
35.
Machine-learning-enabled predictive biomarker discovery and patient stratification using standard-of-care data
The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.
The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method identifying a covariant of interest with respect to drug response phenotype (DRP) of a treatment is disclosed.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
An exemplary discovery platform includes machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method of identifying a patient subgroup of interest, comprises inputting a plurality of medical images obtained from a group of clinical subjects into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space, clustering the plurality of embeddings to generate one or more clusters of embeddings, identifying one or more patient subgroups corresponding to the one or more clusters of embeddings, and associating each patient subgroup of the one or more patient subgroups with a covariant to identify the patient subgroup of interest.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
38.
POOLED OPTICAL SCREENING AND TRANSCRIPTIONAL MEASUREMENTS OF CELLS COMPRISING BARCODED GENETIC PERTURBATIONS
The present disclosure relates to methods of pooled optical screening of genetically barcoded cells comprising genetic perturbations, and simultaneous transcriptional measurements.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
downloadable computer software for performing distributed computing, workflow management, and integrated data source tracking; downloadable computer software for programming and executing statistical analysis of data sets, and for data analysis Drug discovery services; Pharmaceutical drug development services; Software as a service (SaaS) services featuring software for performing distributed computing, workflow management, and integrated data source tracking; software as a service (SaaS) services featuring software for programming and executing statistical analysis of data sets, and for data analysis; Software as a service (SaaS) services featuring software for aggregating high-content data at scale and interpreting it through machine learning
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
downloadable computer software for performing distributed computing, workflow management, and integrated data source tracking; downloadable computer software for programming and executing statistical analysis of data sets, and for data analysis Drug discovery services; Pharmaceutical drug development services; Software as a service (SaaS) services featuring software for performing distributed computing, workflow management, and integrated data source tracking; software as a service (SaaS) services featuring software for programming and executing statistical analysis of data sets, and for data analysis; Software as a service (SaaS) services featuring software for aggregating high-content data at scale and interpreting it through machine learning
41.
MOLECULAR DOCKING-ENABLED MODELING OF DNA-ENCODED LIBRARIES
Embodiments of the disclosure involve training machine learned models using DNA-encoded library experimental data outputs and for deploying the trained machine learned models for conducting a virtual compound screen, for performing a hit selection and analysis, or for predicting binding affinities between compounds and targets. Machine learned models are trained using one or more augmentations that selectively expand molecular representations of a training dataset. Furthermore, machine learned models are trained to account for confounding covariates, thereby improving the machine learned models' abilities to conduct a virtual screen, perform a hit selection, and to predict binding affinities.
Embodiments of the disclosure involve training machine learned models using DNA-encoded library experimental data outputs and for deploying the trained machine learned models for conducting a virtual compound screen, for performing a hit selection and analysis, or for predicting binding affinities between compounds and targets. Machine learned models are trained using one or more augmentations that selectively expand molecular representations of a training dataset. Furthermore, machine learned models are trained to account for confounding covariates, thereby improving the machine learned models' abilities to conduct a virtual screen, perform a hit selection, and to predict binding affinities.
The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time.
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time.
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time.
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G01N 15/14 - Optical investigation techniques, e.g. flow cytometry
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
46.
In situ sequencing of RNA transcripts with non-uniform 5 prime ends
Disclosed herein are methods for performing in situ sequencing of RNA transcripts with non-uniform 5′ ends. During reverse transcription (RT) of RNA transcripts, RT enzyme is induced to “template-switch” to a separate oligonucleotide provided as the template for the upstream flanking region. This flanking region is grafted onto the beginning of the cDNA, enabling padlock probe detection, rolling circle amplification, and fluorescent in situ sequencing. Overall, the disclosed method for in situ sequencing can be applicable for analyzing exogenously introduced transcripts (e.g., identifying and determining impact of a perturbation including a CRISPR perturbation or shRNA/siRNA/ASO perturbation), analyzing naturally occurring transcripts (e.g., measuring gene expression, detecting splicing events), and analyzing modified, naturally occurring transcripts (e.g., detecting mutations or gene edits).
in situin situ in situin situ sequencing can be applicable for analyzing exogenously introduced transcripts (e.g., identifying and determining impact of a perturbation including a CRISPR perturbation or shRNA/siRNA/ASO perturbation), analyzing naturally occurring transcripts (e.g., measuring gene expression, detecting splicing events), and analyzing modified, naturally occurring transcripts (e.g., detecting mutations or gene edits).
Embodiments of the disclosure include methods for implementing a predictive model that predicts pluripotency of cells through a cost efficient and non-destructive means. The predictive model analyzes contrast images captured from the cells and outputs predictions of cellular pluripotency at the cellular level. Thus, implementation of the predictive model guides the selection and isolation of cells that are predicted to be pluripotent. Furthermore, the predictive model facilitates retrospective analyses to correlate pluripotency metrics with differentiation success and further enables tracking of cellular pluripotency over time (e.g., to evaluate differentiation of cells).
The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method of identifying a covariant of interest with respect to a phenotype comprises: receiving covariant information of a covariate class and corresponding phenotypic image data related to the phenotype obtained from a group of clinical subjects; inputting the phenotypic image data into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space, each embedding corresponding to a phenotypic state reflected in the phenotypic image data; and determining, based on the covariant information for the group of clinical subjects, the plurality of embeddings, and one or more linear regression models, an association between each candidate covariant of a plurality of candidate covariants and the phenotype state to identify the covariant of interest.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
50.
POOLED OPTICAL SCREENING AND TRANSCRIPTIONAL MEASUREMENTS OF CELLS COMPRISING BARCODED GENETIC PERTURBATIONS
The present disclosure relates to methods of pooled optical screening of genetically barcoded cells comprising genetic perturbations, and simultaneous transcriptional measurements.
C12Q 1/6881 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time.
Embodiments of the disclosure involve training machine learned models using DNA-encoded library experimental data outputs and for deploying the trained machine learned models for conducting a virtual compound screen, for performing a hit selection and analysis, or for predicting binding affinities between compounds and targets. Machine learned models are trained using one or more augmentations that selectively expand molecular representations of a training dataset. Furthermore, machine learned models are trained to account for confounding covariates, thereby improving the machine learned models' abilities to conduct a virtual screen, perform a hit selection, and to predict binding affinities.
Embodiments of the disclosure involve training machine learned models using DNA-encoded library experimental data outputs and for deploying the trained machine learned models for conducting a virtual compound screen, for performing a hit selection and analysis, or for predicting binding affinities between compounds and targets. Machine learned models are trained using one or more augmentations that selectively expand molecular representations of a training dataset. Furthermore, machine learned models are trained to account for confounding covariates, thereby improving the machine learned models' abilities to conduct a virtual screen, perform a hit selection, and to predict binding affinities.
Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to: compare first nodes of a first call graph to second nodes of a second call graph based, at least in part, on hash values associated with the first and second nodes to identify one or more of the second nodes that are absent from the first nodes.
The present disclosure relates to the electrophoretic sorting of oligonucleotides. The sorted oligonucleotides may be serially enriched and/or used for the synthesis of encoded molecules.
C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA
C07H 21/04 - Compounds containing two or more mononucleotide units having separate phosphate or polyphosphate groups linked by saccharide radicals of nucleoside groups, e.g. nucleic acids with deoxyribosyl as saccharide radical
56.
SYSTEM, DEVICES AND/OR PROCESSES FOR UPDATING CALL GRAPHS
Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to: compare first nodes of a first call graph to second nodes of a second call graph based, at least in part, on hash values associated with the first and second nodes to identify one or more of the second nodes that are absent from the first nodes.
The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method of identifying a covariant of interest with respect to a phenotype comprises: receiving covariant information of a covariate class and corresponding phenotypic image data related to the phenotype obtained from a group of clinical subjects; inputting the phenotypic image data into a trained unsupervised machine-learning model to obtain a plurality of embeddings in a latent space, each embedding corresponding to a phenotypic state reflected in the phenotypic image data; and determining, based on the covariant information for the group of clinical subjects, the plurality of embeddings, and. one or more linear regression models, an association between each candidate covariant of a plurality of candidate covariants and the phenotype state to identify the covariant of interest.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
The present disclosure relates to bivalent or polyvalent linear initiator nucleic acids comprising initial building blocks and a coding region. The linear initiator nucleic acids may be used for the synthesis of an encoded compound to produce bivalent or polyvalent molecules.
Embodiments of the disclosure include methods for implementing a predictive model that predicts pluripotency of cells through a cost efficient and non-destructive means. The predictive model analyzes contrast images captured from the cells and outputs predictions of cellular pluripotency at the cellular level. Thus, implementation of the predictive model guides the selection and isolation of cells that are predicted to be pluripotent. Furthermore, the predictive model facilitates retrospective analyses to correlate pluripotency metrics with differentiation success and further enables tracking of cellular pluripotency over time (e.g., to evaluate differentiation of cells).
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 10/00 - Instruments for taking body samples for diagnostic purposesOther methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determinationThroat striking implements
Provided herein are methods of pooled screening of cells from different genetic backgrounds. Also provided herein are computer-implemented methods for aligning between a first plurality of images and a second plurality of images of biological samples.
C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA
62.
OLIGONUCLEOTIDE DIRECTED AND RECORDED COMBINITORIAL SYNTHESIS OF ENCODED PROBE MOLECULES
The present disclosure relates to multifunctional molecules, including molecules according to formula (I):
The present disclosure relates to multifunctional molecules, including molecules according to formula (I):
([(B1)M-D-L1]Y—H1)O-G-(H2-[L2-E-(B2)K]W)P, (I)
The present disclosure relates to multifunctional molecules, including molecules according to formula (I):
([(B1)M-D-L1]Y—H1)O-G-(H2-[L2-E-(B2)K]W)P, (I)
wherein G, H1, H2, D, E, B1, B2, M, K, L1, L2, O, P, Y, and W are defined herein. The present disclosure also relates to methods of preparing and using such multifunctional molecules to identify encoded molecules capable of binding target molecules.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer software for performing distributed computing, workflow management, and integrated data source tracking; Computer software for programming and executing statistical analysis of data sets, and for data analysis. Software as a service (SaaS) services for performing distributed computing, workflow management, and integrated data source tracking; Software as a service (SaaS) services for programming and executing statistical analysis of data sets, and for data analysis.
64.
METHODS AND SYSTEMS FOR PROCESSING OR ANALYZING OLIGONUCLEOTIDE ENCODED MOLECULES
The present disclosure provides methods and systems for determining a target-activity of at least one resolved oligonucleotide encoded molecule. In an embodiment, a method includes providing a separation medium, wherein the separation medium contains at least one target molecule; and various methods of separating a mixture of at least two oligonucleotide encoded molecules by electrophoresis based on different target-activities of the oligonucleotide encoded molecules for a target molecule. Benefits of the methods disclosed herein can include, without limitation, collecting and calculating qualitative and quantitative data for the target-activity of an encoded portion of the oligonucleotide encoded molecule for a target molecule.
Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.
Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.
G06N 3/04 - Architecture, e.g. interconnection topology
A61B 10/00 - Instruments for taking body samples for diagnostic purposesOther methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determinationThroat striking implements
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
09 - Scientific and electric apparatus and instruments
Goods & Services
downloadable computer software for performing distributed computing, workflow management, and integrated data source tracking; downloadable computer software for programming and executing statistical analysis of data sets, and for data analysis
42 - Scientific, technological and industrial services, research and design
Goods & Services
software as a service (SaaS) services featuring software for performing distributed computing, workflow management, and integrated data source tracking; software as a service (SaaS) services featuring software for programming and executing statistical analysis of data sets, and for data analysis
69.
PREDICTING DISEASE OUTCOMES USING MACHINE LEARNED MODELS
Embodiments of the disclosure include implementing a ML-enabled cellular disease model for validating an intervention, identifying patient populations that are likely responders to an intervention, and developing a therapeutic structure-activity relationship screen. To generate a cellular disease model, data is combined from human genetic cohorts, from the literature, and from general-purpose cellular or tissue-level genomic data to unravel the set of factors (e.g., genetic, environmental, cellular factors) that give rise to a particular disease. In vitro cells are engineered using the set of factors to generate training data for training machine learning models that are useful for implementing cellular disease models.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
e.g.,In vitro In vitro cells are engineered using the set of factors to generate training data for training machine learning models that are useful for implementing cellular disease models.
C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
71.
Molecules for verifying oligonucleotide directed combinatorial synthesis and methods of making and using the same
K-Q-U, wherein G, L, B, K, Q, and U are defined herein. The present disclosure also relates to methods of preparing and using such multifunctional verification molecules to remove defective multifunctional molecules and to quantify synthetic yield.
The present disclosure relates to multifunctional molecules, including molecules according to formula (I-A) [(B1)M-L1]O-G, and (I) [(B1)M-L1]O-G-[(L2-(B2)K]P wherein B1, M, L1, O, G, L2, B2, K, and P are defined herein, wherein each positional building block B1 is identified by from 1 to 5 coding regions in G, and from about 10% to 100% of the positional building blocks B1 at position M and/or B2 at position K, based on a total number of positional building blocks, are identified by a combination of from 2 to 5 independent coding regions. Methods of making such multifunctional molecules, and methods of serially enriching an oligonucleotide encoded library, are also disclosed. The present disclosure also relates to methods of preparing and using such multifunctional molecules to identify encoded molecules capable of binding target molecules.
2, O, P, Y, and W are defined herein. The present disclosure also relates to methods of preparing and using such multifunctional molecules to identify encoded molecules capable of binding target molecules.
C40B 30/04 - Methods of screening libraries by measuring the ability to specifically bind a target molecule, e.g. antibody-antigen binding, receptor-ligand binding
