Pathway recognition algorithm using data integration on genomic models (paradigm)

Abstract

A patient sample specific dynamic pathway map is constructed on the basis of measured patient data and a probabilistic pathway model that is based on attributes for pathway elements, wherein some attributes for pathway elements are known a priori, where other attributes for the pathway elements are assumed, and where the pathway elements are cross-correlated and assigned an influence level for at least one pathway. Preferred dynamic pathway maps provide context of the measured patient data with respect to a selected reference pathway activity.

Description

RELATIONSHIP TO OTHER APPLICATIONS[0001]This application is a division of U.S. Non-provisional patent application Ser. No. 13 / 317,769 entitled “PATHWAY RECOGNITION ALGORITHM USING DATA INTEGRATION ON GENOMIC MODELS (PARADIGM)” filed Oct. 26, 2011, which is incorporated by reference herein, and which is a continuation-in-part application of U.S. Non-provisional patent application Ser. No. 13 / 068,002, entitled “PATHWAY RECOGNITION ALGORITHM USING DATA INTEGRATION ON GENOMIC MODELS (PARADIGM)” filed 29 Apr. 2011, which is incorporated by reference herein, and which is related to and claims priority from U.S. Provisional Patent Application Ser. No. 61 / 343,575 entitled “PATHWAY RECOGNITION ALGORITHM USING DATA INTEGRATION ON GENOMIC MODELS (PARADIGM)” filed 29 Apr. 2010, which is herein incorporated by reference in its entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT[0002]This invention was made partly using funds from the following United States Federal agencies:...

AI Technical Summary

Benefits of technology

The patent describes a method for analyzing biological information by creating a dynamic pathway map (DPM) that integrates information from multiple pathway elements and measurable attributes in a patient sample. The DPM is generated by modifying a probabilistic pathway model with known and assumed attributes and measuring the influence of these attributes on the pathway's activity. The method can be used to analyze various biological samples and can provide valuable information on the pathways involved in health, disease, and other biological processes.

Problems solved by technology

However, while SPIA provided significant advantages in interpreting cancer datasets using pathway topology, SPIA is generally limited to using only a single type of genome-wide data.

Consequently, as information for gene copy number, DNA methylation, somatic mutations, mRNA expression, and microRNA expression are not integrated into SPIA, analytic and predictive value of SPIA remains highly restricted, particularly where a more global analysis is required.

Still further, all or almost all of the currently known pathway analyses fail to incorporate interdependencies among genes in a pathway that can increase the detection signal for pathway relevance.

Therefore, even tough numerous systems and methods of pathway analysis known in the art, all or all of them suffer from one or more disadvantage.

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