Embodiments of the present invention relate generally to non-invasive methods and tests that measure biomarkers (e.g., tumor antigens) and collect clinical parameters from patients, and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, a classifier is generated using a machine learning system based on training data from retrospective data and subset of inputs (e.g. at least two biomarkers and at least one clinical parameter), wherein each input has an associated weight and the classifier meets a predetermined Receiver Operator Characteristic (ROC) statistic, specifying a sensitivity and a specificity, for correct classification of patients. The classifier may then be used to assesses the likelihood that a patient has cancer relative to a population by classify the patient into a category indicative of a likelihood of having cancer or into another category indicative of a likelihood of not having cancer.
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
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
G16B 50/00 - ICT programming tools or database systems specially adapted for bioinformatics
G16B 50/30 - Data warehousingComputing architectures
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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
3.
Methods and machine learning systems for predicting the likelihood or risk of having cancer
Embodiments of the present invention relate generally to non-invasive methods and tests that measure biomarkers (e.g., tumor antigens) and collect clinical parameters from patients, and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, a classifier is generated using a machine learning system based on training data from retrospective data and subset of inputs (e.g. at least two biomarkers and at least one clinical parameter), wherein each input has an associated weight and the classifier meets a predetermined Receiver Operator Characteristic (ROC) statistic, specifying a sensitivity and a specificity, for correct classification of patients. The classifier may then be used to assesses the likelihood that a patient has cancer relative to a population by classify the patient into a category indicative of a likelihood of having cancer or into another category indicative of a likelihood of not having cancer.
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
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
G16B 50/00 - ICT programming tools or database systems specially adapted for bioinformatics
G16B 50/30 - Data warehousingComputing architectures
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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
4.
METHODS AND ALGORITHMS FOR AIDING IN THE DETECTION OF CANCER
A method of data interpretation from a multiplex cancer assay is described. The aggregate normalized score from the assay is transformed to a quantitative risk score quantifying a human subject's increased risk for the presence of cancer as compared to the known prevalence of the cancer in the population before testing the subject.
Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying asymptomatic patients into a risk category for having or developing cancer and/or classifying a patient with an increased risk of having or developing cancer into an organ system-based malignancy class membership and/or into a specific cancer class membership and/or a category with a time range for follow up testing or reclassification with newly measured input factors.
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
G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
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
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
6.
UNIVERSAL PAN CANCER CLASSIFIER MODELS, MACHINE LEARNING SYSTEMS AND METHODS OF USE
Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying asymptomatic patients into a risk category for having or developing cancer and/or classifying a patient with an increased risk of having or developing cancer into an organ system-based malignancy class membership and/or into a specific cancer class membership.
A lung cancer biomarker panel comprising an microRNA (miRNA) lung cancer biomarker and at least one additional lung cancer biomarker selected from a tumor protein (TP) lung cancer biomarker and/or a autoantibody (AAB) lung cancer biomarker is provided herein and methods for screening patients for lung cancer. The present lung cancer biomarker panel provides an improvement in sensitivity and diagnostic accuracy for lung cancer as compared to a lung cancer biomarker panel without the miRNA biomarkers.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
8.
CANCER CLASSIFIER MODELS, MACHINE LEARNING SYSTEMS AND METHODS OF USE
Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying asymptomatic patients into a risk category for having or developing cancer and/or classifying a patient with an increased risk of having or developing cancer into an organ system-based malignancy class membership and/or into a specific cancer class membership.
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
9.
CANCER CLASSIFIER MODELS, MACHINE LEARNING SYSTEMS AND METHODS OF USE
Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying asymptomatic patients into a risk category for having or developing cancer and/or classifying a patient with an increased risk of having or developing cancer into an organ system-based malignancy class membership and/or into a specific cancer class membership.
G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
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 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
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing
G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
10.
Methods and algorithms for aiding in the detection of cancer
A method of data interpretation from a multiplex cancer assay is described. The aggregate normalized score from the assay is transformed to a quantitative risk score quantifying a human subject's increased risk for the presence of cancer as compared to the known prevalence of the cancer in the population before testing the subject.
Methods, kits and reagents are provided for increasing the sensitivity of detecting the presence or absence of endospores by increasing the available protein for detection. The methods are fast and amendable to testing in a non-laboratory setting and use a protein detection reagent and solid microparticles.
C12Q 1/04 - Determining presence or kind of microorganismUse of selective media for testing antibiotics or bacteriocidesCompositions containing a chemical indicator therefor
G01N 33/52 - Use of compounds or compositions for colorimetric, spectrophotometric or fluorometric investigation, e.g. use of reagent paper
G01N 33/68 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing involving proteins, peptides or amino acids
C07H 21/00 - Compounds containing two or more mononucleotide units having separate phosphate or polyphosphate groups linked by saccharide radicals of nucleoside groups, e.g. nucleic acids
C12Q 1/24 - Methods of sampling, or inoculating or spreading a sampleMethods of physically isolating an intact microorganism
C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
Embodiments of the present invention relate generally to non-invasive methods and tests that measure biomarkers (e.g., tumor antigens) and collect clinical parameters from patients, and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, a classifier is generated using a machine learning system based on training data from retrospective data and subset of inputs (e.g. at least two biomarkers and at least one clinical parameter), wherein each input has an associated weight and the classifier meets a predetermined Receiver Operator Characteristic (ROC) statistic, specifying a sensitivity and a specificity, for correct classification of patients. The classifier may then be used to assesses the likelihood that a patient has cancer relative to a population by classify the patient into a category indicative of a likelihood of having cancer or into another category indicative of a likelihood of not having cancer.
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
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
G16B 50/00 - ICT programming tools or database systems specially adapted for bioinformatics
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 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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
13.
METHODS AND COMPOSITIONS FOR AIDING IN DISTINGUISHING BETWEEN BENIGN AND MALIGANNT RADIOGRAPHICALLY APPARENT PULMONRY NODULES
Embodiments of the present invention relate generally to non-invasive methods and diagnostic tests that measure biomarkers (e.g., tumor antigens), clinical parameters and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient with radiographic apparent pulmonary nodules are malignant as compared to benign, relative to a patient population or a cohort population. By utilizing algorithms generated from the biomarker levels (e.g., tumor antigens) from large volumes of longitudinal or prospectively collected blood samples (e.g., real world data from one or more regions where blood based tumor biomarker cancer screening is commonplace) together with one or more clinical parameters (e.g. age, smoking history, disease signs or symptoms) a risk level of that patient having malignant pulmonary nodules is provided.
Methods, kits and reagents are provided for increasing the sensitivity of detecting the presence or absence of endospores by increasing the available protein for detection. The methods are fast and amendable to testing in a non-laboratory setting and use a protein detection reagent and solid microparticles.
C07H 21/00 - Compounds containing two or more mononucleotide units having separate phosphate or polyphosphate groups linked by saccharide radicals of nucleoside groups, e.g. nucleic acids
Embodiments of the present invention relate generally to non-invasive methods and diagnostic tests that measure biomarkers (e.g., tumor antigens), and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, techniques are provided for the use of artificial intelligence / machine learning systems that can incorporate and analyze medical data to perform a risk analysis to determine a likelihood for having cancer. By utilizing algorithms generated from the biomarker levels (e.g., tumor antigens) from large volumes of longitudinal or prospectively collected blood samples (e.g., real world data from one or more regions where blood based tumor biomarker cancer screening is commonplace) together with one or more clinical parameters (e.g. age, smoking history, disease signs or symptoms) a risk level of that patient having a cancer type is provided.
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
16.
METHODS FOR PREDICTING TUMOR RESPONSE TO TARGETED THERAPIES
A method of data interpretation from a multiplex cancer assay is described. The aggregate normalized score from the assay is transformed to a quantitative risk score quantifying a human subject's increased risk for the presence of cancer as compared to the known prevalence of the cancer in the population before testing the subject.
A method of analyzing tissue sections in a manner that provides information about the presence and expression levels of multiple biomarkers at each location within the tissue section. The method comprises the preparation of membranes having covalently bound oligonucleotides and the use of those membranes for evaluation of various markers in the sample. The membranes may be arranged in stacks, wherein each layer has a different oligonucleotide capture strand. Transfer oligonucleotides complementary to the capture strands are attached through a cleavable bond to antibodies that recognize and bind to specific biomarkers present in the tissue sample. The tissue sample is exposed to the antibody-transfer strand conjugate and then treated with a cleaving reagent. Upon cleavage, the transfer strand migrates through the stack and binds to the capture strand. The level of expression of the biomarker may be determined by measuring expression of a reporter on the transfer strand.
01 - Chemical and biological materials for industrial, scientific and agricultural use
Goods & Services
Chemical analysis kits, comprised of a chemical reagent for determining the presence of protein and pH, swabs for protein detection and pH determination, a control swab, and an instruction sheet for use in rapid screening for the presence of biohazardous material used as bioterrorism agents
20.
METHODS AND ALGORITHMS FOR AIDING IN THE DETECTION OF CANCER
A method of data interpretation from a multiplex cancer assay is described. The aggregate normalized score from the assay is transformed to a quantitative risk score quantifying a human subject's increased risk for the presence of cancer as compared to the known prevalence of the cancer in the population before testing the subject.
