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US 2023/0057308 A1Published application
Prediction of biological role of tissue receptors
AssigneeCarmel-Haifa University Economic Corp. Ltd.
InventorJudith SOMEKH
Life of this patent
Estimating the term…
Abstract
Computerized methods to predict or determine if a receptor is associated with a biological process in a target tissue are provided. Methods of training and using a machine learning model to predict or determine if a receptor is associated with a biological process in a target tissue as well as systems and computer program product to do same are also provided.
Claims36
A method comprising:
- training a machine learning model to predict receptors associated with a biological process in a target tissue, on a training set, the method comprising:
- receiving a first list of receptors known to be associated with said biological process and expressed in said target tissue and a second list of receptors known to be expressed in said target tissue and not associated with said biological process;
- receiving a dataset comprising expression profiles in said target tissue for genes encoding proteins, wherein said proteins include receptors of said first list and receptors of said second list;
- applying, to said dataset, gene co-expression network analysis to group said genes into clusters, based on a co-expression relationship between said genes in said target tissue;
- applying to said clusters, a pathways enrichment analysis to assign enrichment scores to said clusters for each pathway of said enrichment analysis, wherein said each pathway is a pathway of a specific biological process;
- labeling receptors from said first list and receptors from said second list with labels comprising said enrichment score for said each pathway assigned to a cluster containing said gene encoding said receptor;
- generating an annotated training set comprising receptors from said first list and receptors from said second list and corresponding labels;
- and training said machine learning model on said annotated training set to produce a trained machine learning model.
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A method comprising:
- receiving, as input, a dataset comprising gene expression profiles with respect to a plurality of genes associated with a corresponding plurality of tissues, wherein at least one of said genes encode a receptor;
- applying, to said dataset, gene co-expression network analysis to group said genes into tissue-specific clusters, based on a co-expression relationship between said genes in tissues of said plurality of tissues;
- applying, to said tissue-specific clusters, a pathways enrichment analysis to assign an enrichment score to said tissue-specific clusters, wherein at least one of said pathways is a pathway of a specific biological process;
- and
- identifying a gene encoding a receptor included in more than one cluster having an enrichment score for a pathway of said specific biological process above a predetermined threshold as a receptor which is associated with said specified biological process.
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A method of determining if a receptor is associated with a biological process in a target tissue, the method comprising:
- receiving, as input, a receptor of unkonwn association with said biological process in said target tissue, and enrichment scores for pathways assigned to a cluster of genes containing a gene encoding said receptor of unknown association, wherein said cluster is generated by gene co-expression network analysis of expression profiles in said target tissue of a set of genes which includes said gene encoding said receptor of unknown association;
- and
- applying a trained machine learning model to said input to determine if said receptor of unknown association is associated with said biological process in said target tissue, wherein said trained machine learning model has been trained on a training set comprising a first list of receptors known to be associated with said biological process and expressed in said target tissue labeled with labels comprising enrichment scores for pathways assigned to a cluster of genes containing a gene encoding said receptors of said first list, and a second list of receptors known to be expressed in said target tissue and not associated with said biological process labeled with labels comprising enrichment scores for pathways assigned to a cluster of genes containing a gene encoding receptors of said second list;
- thereby determining if a receptor is associated with a biological process in a target tissue.
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Dependencies are inferred from the claim text.