Abstract: An approach is provided to computationally identify biomarkers associated with diseases and medical conditions. The procedure first identifies biomarkers individually at the DNA, RNA and proteome levels. Then provides a methodology to integrate the single-source biomarkers and perform dimensionality reduction in order to detect the most informative subset of biomarkers that better distinguish samples between two biological conditions (disease vs normal samples). The dimensionality reduction step minimizing biases due to unnecessary or partially correlated biomarkers and significantly reduces the search space of possible biomarkers. An algorithm is also described for the automated optimization of the proposed DNA-seq and RNA-seq pipelines.

Abstract: An approach is provided to computationally identify biomarkers associated with diseases and medical conditions. The procedure first identifies biomarkers individually at the DNA, RNA and proteome levels. Then provides a methodology to integrate the single-source biomarkers and perform dimensionality reduction in order to detect the most informative subset of biomarkers that better distinguish samples between two biological conditions (disease vs normal samples). The dimensionality reduction step minimizing biases due to unnecessary or partially correlated biomarkers and significantly reduces the search space of possible biomarkers. An algorithm is also described for the automated optimization of the proposed DNA-seq and RNA-seq pipelines.

Abstract: Techniques are disclosed for identifying the likely functionality and sub-cellular localization of individual proteins by first creating a protein-protein interaction network where protein pairs are created from data available from databases and experimental results, and by guessing potential interacting protein pairs where no data exists. Inside each protein pair, mutual likely functionality and localization annotations are made using the known functionalities and localization of the two proteins. The resulting annotated proteins are clustered according to similarity of their annotations and for each cluster iterative mutual annotations in each protein pair enrich the previous functional annotations until no more functionality annotations can be made and results in proteins with at least one assigned functionality and localization duet. Ranking of the resulting assignments is done using the specificity and confidence of the assignment.