Koninklijke Philips Corporation N.V.

Philips Electronics North America Corporation

U.S. Philips Corporation

Funai Electric Company Ltd.

Intertrust Technologies Corporation

Fudan University

Pace, Incorporated

Ipg Electronics 503 Limited

Nevenka Dimitrova

Lalitha Agnihotri

Angel Janevski

Thomas F McGee

Nilanjana Banerjee

Vinay Varadan

Radu S Jasinschi

Sitharthan Kamalakaran

Jan Hermanus Elenbaas

John Doyle Zimmerman

Vartika Agrawal

Serhan Dagtas

Jan Herman Elenbaas

Mauro Barbieri

Jacquelyn Annette Martino

Raymond J Krasinski

Harsh Dhand

Dongge Li

Alexander Ryan Mankovich

Thomas Mc Gee

Konstantin Volyanskyy

Jian Li

Liang Jie Xu

Jarl A Nilsson

J David Schaffer

Christopher Stephen Hall

Kaushal Kurapati

John Ronald Kender

Yasser Alsafadi

Mohamed S Abdel-Mottaleb

Gerhardus Engbertus Mekenkamp

Ying Wu

Lira Nikolovska

Radu Serban Jasinschi

William Knox Carey

Evgeniy Leyvi

Yee Him Cheung

Bart Grantham

James David Schaffer

Edwin Salomons

Henry Lin

Robert Lucito

Yong Mao

Chien Ting Chin

Johanna Maria De Bont

David Berkowitz

Nicholas Joseph Mankovich

Pramod Mayigowda

Chetan Mittal

Sylvie Jeannin

Kostyantyn Volyanskyy

Thomas Francis McGee, Iii

Aaron S Mendelsohn

Karthikeyan Murugesan

Andreas Henricus Elisabeth Lamers

Job Rutgers

Jozef Hieronymus Maria Raijmakers

Gang Wei

Sayan Maity

Mine Danisman Tasar

Hongguang Zhu

Jasper Van Leeuwen

Eugene Leyvi

Yiping Ren

Carolyn Ramsey

Zhaoyong Li

Hansraj Misra

Wilhelmus F J Verhaegh

Narayan Behera

Shruti Sinha

Rakesh Gupta

Sitharthan Kalalakaran

Ann Geoncy

Hansraj Mishra

Lyndsay Harris

Johanna Maria Bont

Lekshmy Parameswaran

Anca Ioana Daniela Bacur

Lalitha Agnibotri

Srinivas R Kudavelly

Systems and methods involve generating a priority score for a variant of a gene based on its potential significance to a disease or other condition. The prevalence of a variant, the mutation rate of a gene containing a variant, and/or pathogenicity scores associated with a variant may be utilized to determine a priority score. Priority scores may be calculated for multiple variants, and the variants may be ranked based on the generated priority scores.

Systems and methods for prioritizing variants of unknown significance

In patient cohort identification, clustering () of patients is performed using a patient comparison metric dependent on a set of features (). 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 () 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 () each plotting the sample patients and the query patient against two or more features of the modality.

Relevance feedback to improve the performance of clustering model that clusters patients with similar profiles together

Methods, systems and apparatus for detecting patterns in constituents of at least one biological organism are disclosed. In accordance with one method, clusters of the constituents are determined by selecting different subsets of at least one of genes or proteins and identifying the clusters from biological data corresponding to the selected subsets. Here, membership values for the constituents, indicating membership within the clusters, are calculated for use as a basis of an additional cluster determination process to obtain final clusters of constituents. By underpinning the preliminary clustering on different subsets of biological data and formulating the higher-level clustering on the basis of the membership values, the embodiments can enable an evaluation of a large variety of biological data in a practical, accurate and highly efficient manner.

Multi-level architecture of pattern recognition in biological data

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. Such populating advantageously re-uses outputs computed for the patient when an analytical module is associated with two or more different clinical questions by the CQ-M matrix. This system empowers the clinician to focus on the clinical aspects of patient management while allowing the data complexities of patient genomic data interpretation to be handled by the clinical decision support system.

System and method for real time clinical questions presentation and management

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 () is configured to execute computer-executable instructions stored in a memory (), the instructions comprising visually representing gene activity level () for at least one gene across a plurality of populations, retrieving a pathway () 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. The instructions further comprise a formal visual grammar representation visually representing information flow () through interactions between genes in pathway, linking the visual representation of the information flow to actionable information () for a clinician, and outputting the visual representation of the information flow and the linked actionable information to the to the clinician.

Pathway visualization for clinical decision support

The present disclosure pertains to a system, a method of using such a system, and a non-transitory computer-readable medium containing instructions to such a system for generating annotated gene fusion data from processing both a patient's DNA and RNA sequence information thereby filtering out weak candidate gene fusions. Thus the annotated gene fusion data contains clinically relevant information and accurate gene fusion detections (low false-positives) for use in clinical and/or R&D settings. The system, method and computer-readable medium allows a user to generate gene fusion data by detecting breakpoints from a patient's DNA-SEQ and RNA-SEQ, creating candidate breakpoint data by combining matching breakpoints from the DNA-SEQ and RNA-SEQ breakpoint data, determining confidence levels of the candidate breakpoint, identifying corresponding gene fusions, and annotating clinically relevant information about the gene fusions.

System and method for detecting gene fusion

The amount of genomic data as well the sensitivity of the information carried necessitates the need to develop smart and efficient ways to transmit genomic data in a secure way. While encryption schemes exist, there is also the need to first reduce the amount of massive information and then apply an encoding and encryption method that will be effective both in the economic sense as well as for security of genomic data. In this invention, we discuss novel techniques to encode processed variant information and send it across to a remote site ensuring covert transmission. The protocols not only encode and encrypts the information; it condenses the information that needs to be transferred.

Secure transmission of genomic data

Methods, systems and apparatus for detecting patterns in constituents of at least one biological organism are disclosed. In accordance with one method, clusters of the constituents are determined () by selecting () different subsets of at least one of genes or proteins and identifying () the clusters from biological data corresponding to the selected subsets. Here, membership values for the constituents, indicating membership within the clusters, are calculated for use as a basis of an additional cluster determination process () to obtain final clusters of constituents. By underpinning the preliminary clustering on different subsets of biological data and formulating the higher-level clustering on the basis of the membership values, the embodiments can enable an evaluation of a large variety of biological data in a practical, accurate and highly efficient manner.

The present disclosure describes systems and methods for generating a priority score for a variant of a gene based on its potential significance to a disease. Priority scores may be calculated for multiple variants, and the variants may be ranked based on the generated priority scores.

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.

Diagnostic genetic analysis using variant-disease association with patient-specific relevance assessment

Methods and systems for identifying causal genetic mechanisms of antibiotic resistance in pathogens. In accordance with at least one embodiment, the system includes a gene resistance module to identify genes present in an antibiotic resistant pathogen, a single nucleotide polymorphism module to identify mutations present in an antibiotic resistant pathogen, and an antibiotic resistance module configured to output the causation of antibiotic resistance based on the identified genes and mutations.

Antibiotic resistance causation identification

A catalog () of molecular marker tests specifies molecular marker tests annotated with clinical applicability annotations. An electronic patient medical record () stores genetic sequencing data () of a patient. A clinical decision support (CDS) system () 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 () 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 () 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.

Retroactive extraction of clinically relevant information from patient sequencing data for clinical decision support

Methods and apparatus for a secure framework for storing and analyzing genomic data. Embodiments of the present invention apply persistent governance to sensitive information and to the analytics that operate upon it, managing the interaction between the two.

Genomic informatics service

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-&#x3b2; signaling pathway. The differential gene expression level information compares baseline gene expression level information from a baseline sample () of a breast tumor of a patient acquired before initiating () a breast cancer therapy regimen to the patient and response gene expression level information from a response sample () 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-&#x3b2; signaling pathway. Related predictive outcome assessment test development methods are also disclosed.

Predictive outcome assessment for chemotherapy with neoadjuvant bevacizumab

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. Furthermore, an assay for detecting, diagnosing, graduating, monitoring or prognosticating a medical condition, or for detecting, 1 diagnosing, monitoring or prognosticating the responsiveness of a subject to a therapy against said medical condition, in particular ovarian cancer, is provided, as well as a corresponding method for classifying a subject comprising and a medical decision support system.

Identification of multi-modal associations between biomedical markers

Growing community of inventors

Yorktown Heights, NY, United States of America

Nevenka Dimitrova