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1.发明公开公开(公告)号：US20240112811A1
公开(公告)日：2024-04-04
申请号：US18130426
申请日：2023-04-04
Applicant: 20/20 GeneSystems Inc.
Inventor： Jonathan Cohen , Jodd Readick , Victoria Doseeva , Peichang SHI , Jose Miguel Flores-Fernandez
CPC classification number: G16H50/30 , G16B40/00 , G16B40/20 , G16B40/30 , G16B50/00 , G16B50/30 , G16H10/60 , G16H50/20 , G16H50/70
Abstract: 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.
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2.发明申请公开(公告)号：US20180068083A1
公开(公告)日：2018-03-08
申请号：US15617899
申请日：2017-06-08
Applicant: 20/20 GeneSystems Inc.
Inventor： Jonathan Cohen , Jodd Readick , Victoria Doseeva , Peichang SHI , Jose Miguel Flores-Fernandez
Abstract: 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.
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Patent Agency Ranking
3.发明授权公开(公告)号：US12051509B2
公开(公告)日：2024-07-30
申请号：US18130426
申请日：2023-04-04
Applicant: 20/20 GeneSystems Inc.
Inventor： Jonathan Cohen , Jodd Readick , Victoria Doseeva , Peichang Shi , Jose Miguel Flores-Fernandez
IPC: G16H50/30 , G16B40/00 , G16B40/20 , G16B40/30 , G16B50/00 , G16B50/30 , G16H10/60 , G16H50/20 , G16H50/70
CPC classification number: G16H50/30 , G16B40/00 , G16B40/20 , G16B40/30 , G16B50/00 , G16B50/30 , G16H10/60 , G16H50/20 , G16H50/70
Abstract: 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.
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4.发明授权公开(公告)号：US11621080B2
公开(公告)日：2023-04-04
申请号：US15617899
申请日：2017-06-08
Applicant: 20/20 GeneSystems Inc.
Inventor： Jonathan Cohen , Jodd Readick , Victoria Doseeva , Peichang Shi , Jose Miguel Flores-Fernandez
IPC: G16H50/30 , G16B40/00 , G16B50/00 , G16H50/20 , G16B40/20 , G16B50/30 , G16B40/30 , G16H10/60 , G16H50/70
Abstract: 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.
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公开(公告)号：US20200005901A1
公开(公告)日：2020-01-02
申请号：US16458589
申请日：2019-07-01
Applicant: 20/20 GeneSystems, Inc
Inventor： Jonathan Cohen , Victoria Doseeva , Peichang Shi
Abstract: 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.
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6.发明申请公开(公告)号：US20190131016A1
公开(公告)日：2019-05-02
申请号：US16089369
申请日：2017-04-01
Applicant: 20/20 GeneSystems Inc.
Inventor： Jonathan Cohen , Victoria Doseeva , Peichang Shi
Abstract: 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.
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公开(公告)号：US20180045730A1
公开(公告)日：2018-02-15
申请号：US15657098
申请日：2017-07-21
Applicant: 20/20 GeneSystems, Inc.
Inventor： Jonathan Cohen , Alexandrine Josephe Derrien-Colemyn , John Gillespie , Soon Sik Park
IPC: G01N33/574 , A61K45/06 , A61K31/436 , A61K38/00
CPC classification number: G01N33/57492 , A61K31/436 , A61K38/00 , A61K45/06 , G01N33/574 , A61K2300/00
Abstract: A method for identifying cancer patients that are likely to be responders or non-responders to a signal transduction pathway inhibitor is described.
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