Abstract: A mechanism is provided for processing electronic files to identify genetic variants of a gene. Evidence of one or more genetic variants of the gene and corresponding information is extracted from a corpus of information. Each genetic variant of the one or more genetic variants is classified based on whether the genetic variant is identified as being pathogenic. Genetic variant annotation is then performed to generate a summary.

Abstract: Computer based methods, systems, and computer readable media are provided for intelligently sorting cells using machine learning. A biological cell analysis sorting machine, wherein the biological cell analysis sorting machine comprises a flow cytometry system and a cell analytics sorting system, may be configured to detect configuration issues by analyzing results of a sorting experiment performed by the biological cell analysis sorting machine. An analysis of a history of prior sorting experiments and associated configuration settings may be performed and a corpus of documents pertaining to the sorting experiment based on the detected configuration issues may be analyzed. Updated configuration settings for the biological cell analysis sorting machine based on the performed analysis may be determined, and the biological cell analysis sorting machine may be configured with the updated configuration settings to conduct a desired sorting experiment.

Abstract: Computer based methods, systems, and computer readable media for intelligently accessing various types of pharmaceutical information in a content repository and ranking drugs at the variant level, gene level, and pathway level. In some cases, drugs that target the same gene, gene variant, or biological pathway may be ranked based upon in vitro, pre-clinical, clinical, or post-clinical evidence. To determine ranking of a plurality of drugs, information pertaining to drug administration is analyzed for the drugs. For a plurality of drugs, attributes corresponding to the drug are determined, wherein the attributes include a variant or a gene targeted by the drug, and a biological pathway comprising the targeted variant or gene. The plurality of drugs are ranked according to a drug effectiveness score based on one or more of a determined efficacy, potency, or toxicity.

Abstract: A mechanism is provided for automated curation of genetic variants using machine learning and natural language processing on multitude sources. A functional study publication in a corpus of information is identified using a supervised classifier. Focal entity detection in the functional study publication is performed by detecting one or more genetic variant mentions in corresponding text using a regular expression based dictionary. Focal genetic variants are identified based on the focal entity detection and based on weighted scores from one or more sections of the corresponding text. For a given identified focal genetic variant, the functional study publication is classified. Sentences in the classified functional study expressing a relation between the given genetic variant and other entities are identified using a relation extraction model. The classified functional study is summarized and facts and relations expressed in the classified functional study are presented.

Abstract: A mechanism is provided for processing electronic files to identify genetic variants of a gene. Evidence of one or more genetic variants of the gene and corresponding information is extracted from a corpus of information. Each genetic variant of the one or more genetic variants is classified based on whether the genetic variant is identified as being pathogenic. Genetic variant annotation is then performed to generate a summary.

Abstract: A genomics artificial intelligence (AI) pipeline comprising a plurality of trained machine learning computer models is provided. First machine learning (ML) computer model(s) extract genomics entities from content of the electronic documents. Second ML computer model(s) determine relationships between genomics entities. Third ML computer model(s) grade biomarkers specified in the relationships based on a predetermined grading scheme and the relationships and gradings are stored in a genomics database for use in processing a patient gene sequencing data structure to identify a signature mutation. A report output is generated identifying the signature mutation present in the patient gene sequencing data structure.

Abstract: Computer based methods, systems, and computer readable media for classifying documents within a content repository or documents within the document subsets are provided. Documents may be pre-processed to render document sections visible to machine readers. Document subsets may be generated based on user-defined terms. The machine readable documents may be classified within the content repository into one of a group of categories, based-upon the number of times classification terms appear in a specific document section of the document. Documents may be ranked based upon the frequency of classification terms in the specific section. Documents may be associated with specific diseases such as cancer, genes, gene variants, and drugs or synonyms thereof by comparing relevant search terms to specific sections of the documents.

Abstract: Computer-based methods, systems, and computer readable media for managing documents within a content repository or documents within the document subsets are provided. Documents within the content repository may be classified into one of a functional category and a clinical category. Documents are applied to a machine learning annotation and analysis module to automatically annotate the documents to indicate relationships between entities. A request is processed for the documents including one or more search terms, wherein the search terms pertain to one or more entities from a group of gene, gene variant, drug, cancer and a biomedical/clinical term. Documents satisfying the request are identified by comparing the one or more search terms to the annotations and specific sections of the documents, and determining a relevance of a document based on the comparison and a frequency of the one or more search terms in each of the specific sections. The identified documents are ranked according to custom techniques.

Abstract: A computer system processes documents in a content repository. Each document of a plurality of documents is classified into one of a functional category and a clinical category. Each document is annotated using one or more corpora to generate document annotations. Documents satisfying one or more query terms are identified by comparing each query term to the document annotations. The identified documents are ranked based on a determined relevance. Guidelines are produced based on the ranking of the identified documents. Embodiments of the present invention further include a method and program product for processing documents in a content repository in substantially the same manner described above.

Abstract: Computer based methods, systems, and computer readable media are provided for intelligently sorting cells using machine learning. A biological cell analysis sorting machine, wherein the biological cell analysis sorting machine comprises a flow cytometry system and a cell analytics sorting system, may be configured to detect configuration issues by analyzing results of a sorting experiment performed by the biological cell analysis sorting machine. An analysis of a history of prior sorting experiments and associated configuration settings may be performed and a corpus of documents pertaining to the sorting experiment based on the detected configuration issues may be analyzed. Updated configuration settings for the biological cell analysis sorting machine based on the performed analysis may be determined, and the biological cell analysis sorting machine may be configured with the updated configuration settings to conduct a desired sorting experiment.

Abstract: A computer system processes documents in a content repository. Each document of a plurality of documents is classified into one of a functional category and a clinical category. Each document is annotated using one or more corpora to generate document annotations. Documents satisfying one or more query terms are identified by comparing each query term to the document annotations. The identified documents are ranked based on a determined relevance. Guidelines are produced based on the ranking of the identified documents. Embodiments of the present invention further include a method and program product for processing documents in a content repository in substantially the same manner described above.

Abstract: Techniques are provided for administering combination of drug treatments to a patient. Information is analyzed pertaining to individual drug treatments from structurally or functionally defined drugs, drugs with unknown functions, and corresponding effects, wherein the information includes omic data including genes, transcripts, proteins, as well as experimental data from published documents. One or more combinations of drug treatments are identified with combined effects producing a positive result, wherein the positive result is directed to a specific aspect of patient health. The identified combination of drug treatments are administered to a patient.

Abstract: Computer based methods, systems, and computer readable media for intelligently accessing various types of pharmaceutical information in a content repository and ranking drugs at the variant level, gene level, and pathway level. In some cases, drugs that target the same gene, gene variant, or biological pathway may be ranked based upon in vitro, pre-clinical, clinical, or post-clinical evidence. To determine ranking of a plurality of drugs, information pertaining to drug administration is analyzed for the drugs. For a plurality of drugs, attributes corresponding to the drug are determined, wherein the attributes include a variant or a gene targeted by the drug, and a biological pathway comprising the targeted variant or gene. The plurality of drugs are ranked according to a drug effectiveness score based on one or more of a determined efficacy, potency, or toxicity.

Abstract: Computer-based methods, systems, and computer readable media for managing documents within a content repository or documents within the document subsets are provided. Documents within the content repository may be classified into one of a functional category and a clinical category. Documents are applied to a machine learning annotation and analysis module to automatically annotate the documents to indicate relationships between entities. A request is processed for the documents including one or more search terms, wherein the search terms pertain to one or more entities from a group of gene, gene variant, drug, cancer and a biomedical/clinical term. Documents satisfying the request are identified by comparing the one or more search terms to the annotations and specific sections of the documents, and determining a relevance of a document based on the comparison and a frequency of the one or more search terms in each of the specific sections. The identified documents are ranked according to custom techniques.

Abstract: Computer based methods, systems, and computer readable media for classifying documents within a content repository or documents within the document subsets are provided. Documents may be pre-processed to render document sections visible to machine readers. Document subsets may be generated based on user-defined terms. The machine readable documents may be classified within the content repository into one of a group of categories, based-upon the number of times classification terms appear in a specific document section of the document. Documents may be ranked based upon the frequency of classification terms in the specific section. Documents may be associated with specific diseases such as cancer, genes, gene variants, and drugs or synonyms thereof by comparing relevant search terms to specific sections of the documents.

Abstract: Methods, systems, and computer readable media are provided for processing microscopic images of a biological sample from a patient. One or more images of a blood sample from a microscope is obtained, each image comprising a plurality of different types of cells. The one or more images are processed by a machine learning system to classify individual cells into one of a plurality of cell categories. The cells in each cell category are analyzed to determine characteristics of the respective cell category. A diagnosis or list of possible diagnosis are determined based on the classification and characteristics of the cells for the patient in an automated manner.

Abstract: According to embodiments of the present invention, methods, systems and computer readable media are provided for extracting related medical information from various sources to produce a medical evaluation. Genomic information provided from a patient tumor sample is analyzed to determine the presence of one or more mutations in the tumor sample. Hierarchical matching is performed to match the one or more mutations from the patient sample to curated structured data derived from literature. One or more of a prognosis, diagnosis, or predisposition is evaluated based on the matching, wherein the one or more mutations is predictive of a prognosis for a type of tumor, and is a diagnostic marker of a type of tumor. When a pathogenic mutation is detected for a predisposition, a report is generated regarding whether the pathogenic mutation is associated with hereditary cancer.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for automatically classifying mutations using a table of knowledge in the format of a hierarchical classification table, without need for manual curation by genomics domain subject matter experts (SMEs) one at a time. A query from a user to classify a mutation is received. Mutations are matched to one or more entries in the table of known mutation classifications based on a name, a description, or a range of a gene sequence, or a combination thereof. The closest matched entry to the mutation is determined. The mutation is classified using the classification of the closest matched entry in the table.