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 computer system updates a knowledge graph. A model corresponding to a set of documents is received, wherein the model comprises a plurality of entities, a plurality of entity associations, and a plurality of confidence scores corresponding to the plurality of entity associations. A relevance value is calculated for each entity of the plurality of entities that are present in the set of documents and for each entity of the plurality of entities that are present in a new document. One or more entity associations that are supported by specific portions of the new document are identified. The confidence scores for each of the identified one or more entity associations are updated based on a level of support in the new document. Embodiments of the present invention further include a method and program product for updating a knowledge graph in substantially the same manner described above.

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: Computer-based methods, systems, and computer readable media for managing documents within a content repository or documents within the document subsets are provided. Documents may be pre-processed to be machine readable and classified within the content repository into one or more categories, based upon a number of times classification terms appear in a specific section of the document or based on an article type tag. Document subsets may be generated based on user-defined terms. Documents may be associated with specific cancer-types, genes, gene variants and drugs by comparing relevant search terms to specific sections of the documents. A request for processing the documents may include one or more of the search terms, pertaining to one or more from a group of gene, gene variant, drug, and cancer terms.

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: Computer-based methods, systems, and computer readable media for managing documents within a content repository or documents within the document subsets are provided. Documents may be pre-processed to be machine readable and classified within the content repository into one or more categories, based upon a number of times classification terms appear in a specific section of the document or based on an article type tag. Document subsets may be generated based on user-defined terms. Documents may be associated with specific cancer-types, genes, gene variants and drugs by comparing relevant search terms to specific sections of the documents. A request for processing the documents may include one or more of the search terms, pertaining to one or more from a group of gene, gene variant, drug, and cancer terms.

Abstract: A method, system, and computer program product are provided for generating a container providing a computing environment. At least one processing device combines a base image of a first type of container, including at least one application and an operating system, with a base image of a second type of container including middleware and configuration information to produce a base image of a target container including, from the first type of container, the at least one application and the operating system, and the middleware and the configuration information from the second type of container. The base image of the target container is executed on a computer to provide the computing environment configured in accordance with the configuration information.

Abstract: A computer system updates a knowledge graph. A model corresponding to a set of documents is received, wherein the model comprises a plurality of entities, a plurality of entity associations, and a plurality of confidence scores corresponding to the plurality of entity associations. A relevance value is calculated for each entity of the plurality of entities that are present in the set of documents and for each entity of the plurality of entities that are present in a new document. One or more entity associations that are supported by specific portions of the new document are identified. The confidence scores for each of the identified one or more entity associations are updated based on a level of support in the new document. Embodiments of the present invention further include a method and program product for updating a knowledge graph in substantially the same manner described above.

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: A method, system, and computer program product are provided for generating a container providing a computing environment. At least one processing device combines a base image of a first type of container, including at least one application and an operating system, with a base image of a second type of container including middleware and configuration information to produce a base image of a target container including, from the first type of container, the at least one application and the operating system, and the middleware and the configuration information from the second type of container. The base image of the target container is executed on a computer to provide the computing environment configured in accordance with the configuration information.

Abstract: Embodiments describe an approach for improving eligibility criteria matching for clinical trials, the method comprising searching one or more proposed clinical trials, wherein the one or more proposed clinical trials comprises: a condition group, an intervention group and inclusion/exclusion criteria in the hierarchy structure. Determining if a patient's clinical information matches the one or more proposed clinical trial data. Responsive to determining a match between the patient clinical information matching and the one of the one or more proposed clinical trial data, wherein the matching comprises parent and child relationships for one or more patient clinical information, creating an entry in a clinical trial database based on the one or more proposed clinical trials and the patient clinical information, and outputting one or more clinical trials that match the patient clinical information in a structured format.

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.