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VIStA -- unsuperVIsed Shuffling Approach

Technology Benefits

The approach:ΓÇóEffectively divides a set of patients into different groups with a large number of differentially expressed genesΓÇóFurther identifies/pinpoints various clinical characteristics that differ significantly between groupsΓÇóAvoids a need for prior clinical patient information, enabling a complete unbiased assessmentΓÇóAvoids use of a predefined division of patients into case and control as required for conventional approachesΓÇóWould be commercially useful for the following applications:oIdentification of novel disease biomarkersoIdentification of subtypes of complex diseasesoIndividualized diagnosis and treatment for personal medicine

Detailed Technology Description

None

*Abstract

Conventional patient clustering approaches rely on prior clinical characteristics. Such methods cluster patients using pair-wise similarity expression patterns. As a result, these existing approaches allow for a biased division of patients, which at times is highly undesirable. This invention discloses a novel approach (unsupervised shuffling approach) that uses an algorithm to identify subgroup of patients with maximal difference in gene expression patterns, capable of overcoming prior-art limitations and/or unmet needs.

*Principal Investigator

Name: Albert-Laszlo Barabasi

Department:

Name: Jorg Menche

Department:

Country/Region

USA