Abstract: The present disclosure generally relates to the treatment of subjects having a stroke or at risk of developing a stroke by administering a 1,4-alpha-glucan-branching enzyme (GBE1) inhibitor to the subject.

Abstract: In patient cohort identification, clustering (30) of patients is performed using a patient comparison metric dependent on a set of features (24). Information is displayed on sample patients who are similar or dissimilar to a query patient according to the clustering. User inputted comparison values are received comparing the sample patients with the query patient. The set of features and/or feature weights are adjusted to generate an adjusted patient comparison metric having improved agreement with the user inputted comparison values. The clustering is repeated using the adjusted patient comparison metric. A patient cohort is identified from a cluster (34) containing the query patient produced by the last clustering repetition. The information on the sample patients may be shown by simultaneously displaying two or more graphical modality representations (70, 72, 74) each plotting the sample patients and the query patient against two or more features of the modality.

Abstract: A method (10) for forming novel signatures of biological data is provided. The method comprises ranking features based on a trend value, which is created based on multiple signatures identified by a pattern discovery method. Furthermore, a device (30) and a computer program product (40), performing the steps according to the method (10) is provided. Uses of the method, for statistically analyzing clinical data, designing assays based on multiple molecular signatures and interpreting assays based on multiple molecular signatures are also provided.

Abstract: In a clinical decision support method, outputs of computer-implemented analytical modules are computed for a patient. Information is displayed for the patient pertaining to a clinical question comprising outputs computed for the patient of analytical modules associated with the clinical question. The analytical modules may include modules configured to perform in silico genetic/genomic tests using genetic/genome sequencing (whole genome, whole exome, whole transcriptome, targeted gene panels, etc) or microarray data. A clinical question-module matrix (CQ-M matrix) may be generated for the patient associating clinical questions with analytical modules, and the method may further include populating the clinical questions with outputs computed for the patient of the analytical modules associated with the clinical questions by the CQ-M matrix.

Abstract: When generating visual representations of gene activity pathways for clinical decision support, a validated pathway database that stores a plurality of validated pathways is accessed, wherein each pathway describes at least one interaction between a plurality of genes. A processor (18) is configured to execute computer-executable instructions stored in a memory (16), the instructions comprising visually representing gene activity level (28) for at least one gene across a plurality of populations, retrieving a pathway (32) from the validated pathway database, wherein the pathway includes the at least one gene, and visually representing gene activity levels for all genes in the pathway.

Abstract: When generating visual representations of gene activity pathways for clinical decision support, a validated pathway database that stores a plurality of validated pathways is accessed, wherein each pathway describes at least one interaction between a plurality of genes. A processor (18) is configured to execute computer-executable instructions stored in a memory (16), the instructions comprising visually representing gene activity level (28) for at least one gene across a plurality of populations, retrieving a pathway (32) from the validated pathway database, wherein the pathway includes the at least one gene, and visually representing gene activity levels for all genes in the pathway.

Abstract: The present invention relates to effective diagnosis of patients and assisting clinicians in treatment planning. In particular, invention provides a medical analysis system that enables refinement of molecular classification. The system provides a molecular profiling solution that will allow improved diagnosis, prognosis, response prediction to provide the right chemotherapy, and follow-up to monitor for cancer recurrence.

Abstract: Relevance of a study genetic variant observed in diagnostic subject genetic data that is associated by a clinical study with a phenotype characteristic is assessed as follows. A set of polymorphisms functionally related to the study genetic variant are identified. A foreground distribution is computed of variants observed in the diagnostic subject genetic data for the set of polymorphisms. A background distribution is computed of variants observed in genetic data of subjects of the clinical study for the set of polymorphisms. A comparison metric is computed comparing the foreground distribution and the background distribution. Relevance of the study variant to the diagnostic subject is quantified based on the comparison metric, with higher similarity of the foreground and background distributions corresponding to higher relevance.

Abstract: A catalog (34) of molecular marker tests specifies molecular marker tests annotated with clinical applicability annotations. An electronic patient medical record (22) stores genetic sequencing data (20) of a patient. A clinical decision support (CDS) system (30) is configured to track the clinical context of the patient wherein the clinical context includes at least a disease diagnosis and a current patient care stage. A catalog search module (32) is configured to search the catalog of molecular marker tests to identify a molecular marker test having clinical applicability to the patient in the clinical context tracked by the CDS system. The search is automatically triggered by occurrence of a trigger event defined by a set of triggering rules. A testing module (44) is configured to perform a molecular marker test identified by the identification module in silico using the genetic sequencing data of the patient stored in the electronic patient medical record.

Abstract: In a predictive outcome assessment test for predicting whether a patient undergoing a breast cancer treatment regimen will achieve pathological complete response (pCR), differential gene expression level information are generated for an input set of genes belonging to the TGF-? signaling pathway. The differential gene expression level information compares baseline gene expression level information from a baseline sample (70) of a breast tumor of a patient acquired before initiating (71) a breast cancer therapy regimen to the patient and response gene expression level information from a response sample (72) of the breast tumor acquired after initiating the breast cancer therapy regimen by administering a first dose of bevacizumab to the patient. A pCR prediction for the patient is computed based on the differential gene expression level information for the input set of genes belonging to the TGF-? signaling pathway. Related predictive outcome assessment test development methods are also disclosed.

Abstract: The present invention relates to a method for identifying multi-modal associations between biomedical markers which allows for the determination of network nodes and/or high ranking network members or combinations thereof, indicative of having a diagnostic, prognostic or predictive value for a medical condition, in particular ovarian cancer. The present invention further relates to a biomedical marker or group of biomedical markers associated with a high likelihood of responsiveness of a subject to a cancer therapy, preferably a platinum based cancer therapy, wherein said biomedical marker or group of biomedical markers comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 8, 19, 20 or all markers selected from PKMYT1, SKIL, RAB8A, HIRIP3, CTNNB1, NGFR, ZCCHC11, LSP1, CD200, PAX8, CYBRD1, HOXC11, TCEAL1, FZD10, FZD1, BBS4, IRS2, TLX3, TSPAN2, TXN, and CFLAR.

Abstract: In patient cohort identification, clustering (30) of patients is performed using a patient comparison metric dependent on a set of features (24). Information is displayed on sample patients who are similar or dissimilar to a query patient according to the clustering. User inputted comparison values are received comparing the sample patients with the query patient. The set of features and/or feature weights are adjusted to generate an adjusted patient comparison metric having improved agreement with the user inputted comparison values. The clustering is repeated using the adjusted patient comparison metric. A patient cohort is identified from a cluster (34) containing the query patient produced by the last clustering repetition. The information on the sample patients may be shown by simultaneously displaying two or more graphical modality representations (70, 72, 74) each plotting the sample patients and the query patient against two or more features of the modality.

Abstract: A data-driven integrative visualization system and a method for visualization and exploration of the multi-modal features of a cohort of samples, is disclosed Specifically, a method for providing an interactive computation and visualization front-end of a genomics platform for presenting the complex multiparametric and high dimensional, multi-omic data of a patient with respect to a cohort of samples, that assists the user in understanding the similarities and differences across individual or groups of samples, identify correlation among different features and improve treatment planning and long term patient care, is described. The method may include obtaining and inputting multi-omic data of a patient and/or cohorts, identifying multi-modal feature variations and their relationships, and displaying this information in an interactive circular format on a GUI, from which the user can access further information.

Abstract: A system and method for determining the functional impact of somatic mutations and genomic aberrations on downstream cellular processes by integrating multi-omics measurements in cancer samples with community-curated biological pathways are disclosed.