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1.发明申请公开(公告)号：US20210103642A9
公开(公告)日：2021-04-08
申请号：US15960445
申请日：2018-04-23
摘要： The present invention provides methods and compositions related to genomic profiling, and in particular, to assigning probabilistic measure of clinical outcome for a patient having a disease or a tumor using segmented genomic profiles such as those produced by representational oligonucleotide microarray analysis (ROMA).
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公开(公告)号：US20200285714A9
公开(公告)日：2020-09-10
申请号：US16030296
申请日：2018-07-09
摘要： Systems and methods for diagnosing prostate cancer. Image sets (e.g., MRI collected at one or more b-values) and biological values (e.g., prostate specific antigen (PSA)) have features extracted and integrated to produce a diagnosis of prostate cancer. The image sets are analyzed primarily in three steps: (1) segmentation, (2) feature extraction, smoothing, and normalization, and (3) classification. The biological values are analyzed primarily in two steps: (1) feature extraction and (2) classification. Each analysis results in diagnostic probabilities, which are then combined to pass through an additional classification stage. The end result is a more accurate diagnosis of prostate cancer.
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公开(公告)号：US20200012761A1
公开(公告)日：2020-01-09
申请号：US16030296
申请日：2018-07-09
摘要： Systems and methods for diagnosing prostate cancer. Image sets (e.g., MRI collected at one or more b-values) and biological values (e.g., prostate specific antigen (PSA)) have features extracted and integrated to produce a diagnosis of prostate cancer. The image sets are analyzed primarily in three steps: (1) segmentation, (2) feature extraction, smoothing, and normalization, and (3) classification. The biological values are analyzed primarily in two steps: (1) feature extraction and (2) classification. Each analysis results in diagnostic probabilities, which are then combined to pass through an additional classification stage. The end result is a more accurate diagnosis of prostate cancer.
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公开(公告)号：US20190258776A1
公开(公告)日：2019-08-22
申请号：US15900048
申请日：2018-02-20
发明人： FILIPPO UTRO , ALDO GUZMAN SAENZ , CHAYA LEVOVITZ , LAXMI PARIDA
摘要： A computer-implemented method includes generating, by a processor, a set of training data for each phenotype in a database including a set of subjects. The set of training data is generated by dividing genomic information of N subjects selected with or without repetition into windows, computing a distribution of genomic events in the windows for each of N subjects, and extracting, for each window, a tensor that represents the distribution of genomic events for each of N subjects. A set of test data is generated for each phenotype in the database, a distribution of genomic events in windows for each phenotype is computed, and a tensor is extracted for each window that represents a distribution of genomic events for each phenotype. The method includes classifying each phenotype of the test data with a classifier, and assigning a phenotype to a patient.
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5.发明申请公开(公告)号：US20190243943A9
公开(公告)日：2019-08-08
申请号：US16006593
申请日：2018-06-12
CPC分类号： G16B45/00 , C12Q1/6886 , C12Q2600/156 , C12Q2600/158 , G06F16/285 , G06F17/18 , G16B5/00 , G16B20/00 , G16B40/00 , G16B50/00 , G16H10/20 , G16H20/00 , G16H20/10 , G16H20/40 , G16H50/20 , G16H50/30 , G16H50/50 , G16H50/70 , G16H70/20
摘要： Various methods, systems, computer readable media, and graphical user interfaces (GUIs) are presented and described that enable a subject, doctor, or user to characterize or classify various types of cancer precisely. Additionally, described herein are methods, systems, computer readable media, and GUIs that enable more effective specification of treatment and improved outcomes for patients with identified types of cancer. Some embodiments of the methods, systems, computer readable media, and GUIs described herein comprise obtaining RNA expression data and/or whole exome sequencing (WES) data for a biological sample; determining a molecular-functional (MF) profile using the data; determining sets of visual characteristics for GUI elements using the data; generating a personalized GUI using the determined visual characteristics; and presenting the generated personalized GUI to a user.
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6.发明申请公开(公告)号：US20190221309A1
公开(公告)日：2019-07-18
申请号：US15871159
申请日：2018-01-15
申请人： Jang-Jih Lu , Chun-Hsien Chen , Hsin-Yao Wang , Yi-Hsin Chan , Wei-Shang Shih
发明人： Jang-Jih Lu , Chun-Hsien Chen , Hsin-Yao Wang , Yi-Hsin Chan , Wei-Shang Shih
CPC分类号： G16H50/20 , C12Q1/6883 , G01N33/5023 , G01N2800/32 , G01N2800/50 , G16B40/00 , G16H50/30 , G16H50/50
摘要： A coronary artery disease (CAD) screening method includes 1) collecting clinical information of asymptomatic individuals and testing a plurality of samples of the individuals by using a cardiovascular markers panel including a plurality of cardiovascular markers; 2) entering the clinical information and the test results and the corresponding CAD states of the individuals into a machine learning platform; 3) selecting a plurality of roust variables from the clinical information and the cardiovascular markers of the cardiovascular markers panel by using feature selection methods; 4) using a machine learning algorithm embedded in the machine learning platform to establish a CAD prediction model; and 5) entering clinical information and sample data obtained by using the cardiovascular markers panel for an individual being screened into the CAD prediction model for calculation and analysis, thereby determining whether the individual being screened has CAD or not.
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公开(公告)号：US10338074B2
公开(公告)日：2019-07-02
申请号：US15051153
申请日：2016-02-23
申请人： Biodesix, Inc.
IPC分类号： G01N33/574 , G01N33/68 , G06F19/18 , G06F19/24
摘要： Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).
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公开(公告)号：US10309968B2
公开(公告)日：2019-06-04
申请号：US15599431
申请日：2017-05-18
发明人： Ngoc Hieu Tran , Mohammad Ziaur Rahman , Lin He , Lei Xin , Baozhen Shan , Ming Li
摘要： Methods and systems for determining amino acid sequence of a polypeptide or protein from mass spectrometry data is provided, using a weighted de Bruijn graph. Extracted and purified protein is cleaved into a mixture of peptide and then analyzed using mass spectrometry. A list of peptide sequences is derived from mass spectrometry fragment data by de novo sequencing, and amino acid confidence scores are determined from peak fragment ion intensity. A weighted de Bruijn graph is constructed for the list of peptide sequences having node weights defined by k−1 mer confidence scores. At least one contig is assembled from the de Bruijn graph by identifying node weights having the highest k−1 mer confidence scores.
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公开(公告)号：US20190130064A1
公开(公告)日：2019-05-02
申请号：US15796679
申请日：2017-10-27
发明人： Ian M. Kerman , Kristine Briedis , Scott Markel , Dave Rogers
摘要： In accordance with one embodiment of the invention, features of biological sequences are represented in a fingerprint that includes a bitset, and may also include counts, strings or continuous values, for the features. The fingerprint can be used with machine learning and statistical methods. This is especially advantageous for, though not limited to, drug discovery processes. The method permits Structure-Activity Relationship (SAR) and Quantitative Structure-Activity Relationship (QSAR) studies to be performed with biological sequences.
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公开(公告)号：US20190114390A1
公开(公告)日：2019-04-18
申请号：US16160457
申请日：2018-10-15
申请人： BioAge Labs, Inc.
摘要： A deep learning model measures functional similarities between compounds based on gene expression data for each compound. The model receives an unlabeled expression profile for a query perturbagen including transcription counts of a plurality of genes in a cell affected the query perturbagen. The model extracts an embedding of the expression profile. Using the embedding of the query perturbagen and embeddings of known perturbagens, the model determines a set of similarity scores, each indicating a likelihood that a known perturbagen has a similar effect on gene expression as the query perturbagen. The likelihood, additionally, provides a prediction that the known perturbagen and query perturbagen share pharmacological similarities. The similarity scores are ranked and, from the ranked set, at least one candidate perturbagen is determined to be pharmacologically similar to the query perturbagen. The model may further be applied to determine similarities in structure and biological protein targets between perturbagens.
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