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公开(公告)号：US20190295684A1
公开(公告)日：2019-09-26
申请号：US16359385
申请日：2019-03-20
Applicant: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
Inventor： Ari Allyn-Feuer , Brian D. Athey , Gerald A. Higgins , Alex Ade
Abstract: To analyze spatial organization of chromatin a computing device may compile genomic element contacts or reads into variable size bins using a binary search tree. The bins may be selected to each represent a different cutsite increment or functional element within a genome, such as a gene, TAD, chromatin state segment, loop domain, chromatin domain, etc. Two sets of bins are selected to generate a squared genome matrix of bin pairs, where each set represent an axis of the matrix. Then a normalization method is applied to the interaction frequencies for the bin pairs having variable size and/or shape to generate normalized interaction frequencies for each bin pair. The normalized interaction frequencies may be used to identify bin pairs having enriched and depleted contacts for a variety of analyses, including the detection of target genes of genomic variants, as well as genome wide analysis of contacts.
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公开(公告)号：US10553318B2
公开(公告)日：2020-02-04
申请号：US16267546
申请日：2019-02-05
Applicant: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
Inventor： Brian D. Athey , Ari Allyn-Feuer , Gerald A. Higgins , James S. Burns , Alexandr Kalinin , Brian Pauls , Alex Ade , Narathip Reamaroon
IPC: G16H50/20 , G16H50/30 , G16H10/60 , A61K31/37 , G06N3/08 , G16H50/70 , G16B30/00 , G16B40/00 , G16B20/00
Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.
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Patent Agency Ranking
公开(公告)号：US12154661B2
公开(公告)日：2024-11-26
申请号：US16359385
申请日：2019-03-20
Applicant: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
Inventor： Ari Allyn-Feuer , Brian D. Athey , Gerald A. Higgins , Alex Ade
Abstract: To analyze spatial organization of chromatin a computing device may compile genomic element contacts or reads into variable size bins using a binary search tree. The bins may be selected to each represent a different cutsite increment or functional element within a genome, such as a gene, TAD, chromatin state segment, loop domain, chromatin domain, etc. Two sets of bins are selected to generate a squared genome matrix of bin pairs, where each set represent an axis of the matrix. Then a normalization method is applied to the interaction frequencies for the bin pairs having variable size and/or shape to generate normalized interaction frequencies for each bin pair. The normalized interaction frequencies may be used to identify bin pairs having enriched and depleted contacts for a variety of analyses, including the detection of target genes of genomic variants, as well as genome wide analysis of contacts.
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公开(公告)号：US10915729B2
公开(公告)日：2021-02-09
申请号：US16277128
申请日：2019-02-15
Applicant: The Regents of The University of Michigan
Inventor： Ivaylo Dinov , Brian D. Athey , David S. Dilworth , Ari Allyn-Feuer , Alexandr Kalinin , Alex S. Ade
Abstract: The ability to automate the processes of specimen collection, image acquisition, data pre-processing, computation of derived biomarkers, modeling, classification and analysis can significantly impact clinical decision-making and fundamental investigation of cell deformation. This disclosure combine 3D cell nuclear shape modeling by robust smooth surface reconstruction and extraction of shape morphometry measure into a highly parallel pipeline workflow protocol for end-to-end morphological analysis of thousands of nuclei and nucleoli in 3D. This approach allows efficient and informative evaluation of cell shapes in the imaging data and represents a reproducible technique that can be validated, modified, and repurposed by the biomedical community. This facilitates result reproducibility, collaborative method validation, and broad knowledge dissemination.
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公开(公告)号：US20190172584A1
公开(公告)日：2019-06-06
申请号：US16267546
申请日：2019-02-05
Applicant: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
Inventor： Brian D. Athey , Ari Allyn-Feuer , Gerald A. Higgins , James S. Burns , Alexandr Kalinin , Brian Pauls , Alex Ade , Narathip Reamaroon
IPC: G16H50/20 , G16B40/00 , G16B30/00 , A61K31/37 , G16B20/00 , G06N3/08 , G16H10/60 , G16H50/30 , G16H50/70
Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.
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公开(公告)号：US10249389B2
公开(公告)日：2019-04-02
申请号：US15977347
申请日：2018-05-11
Applicant: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
Inventor： Brian D. Athey , Ari Allyn-Feuer , Gerald A. Higgins , James S. Burns , Alexandr Kalinin , Brian Pauls , Alex Ade , Narathip Reamaroon
IPC: G06N3/08 , A61K31/37 , G06F19/18 , G06F19/22 , G06F19/24 , G16H10/60 , G16H50/20 , G16H50/30 , G16H50/70
Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic, physiomic, environmental, sociomic, demographic, and outcome phenotype data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.
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公开(公告)号：US20180330824A1
公开(公告)日：2018-11-15
申请号：US15977347
申请日：2018-05-11
Applicant: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
Inventor： Brian D. Athey , Ari Allyn-Feuer , Gerald A. Higgins , James S. Burns , Alexandr Kalinin , Brian Pauls , Alex Ade , Narathip Reamaroon
CPC classification number: G16H50/20 , A61K31/37 , G06F19/18 , G06F19/22 , G06F19/24 , G06N3/08 , G16H10/60 , G16H50/30 , G16H50/70
Abstract: For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic, physiomic, environmental, sociomic, demographic, and outcome phenotype data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.
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