Gene Functional Prediction from Protein-Protein Interaction Data

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The Florida International University has been awarded a grant to develop computational tools to help predict protein - protein interactions. Proteins interact with other proteins either in pairs or as components of larger complexes in many cellular processes, including metabolism, endocrine and exocrine, signaling, synthesis, and transport. A systematic identification, characterization and understanding of protein-protein interactions will provide an essential knowledge base, and link proteome dynamics and architecture to cellular function and phenotype. One of the most important applications of analyzed PPI data is to predict functional annotation for uncharacterized proteins. Uncharacterized proteins (also called unannotated proteins) refer to proteins for which we do not know their functions. Functional annotation of proteins is a fundamental problem in the post-genomic era. However, currently available computational tools for functional annotation using PPI need to be improved. To help solve this problem this project will developing advanced clustering methods for analyzing protein-protein interaction (PPI) data and will develop computational methods for integrating gene expression profiles and protein-protein interaction data which will improve the robustness and accuracy of predicting protein interaction modules. The project will Investigate and develop methods for evaluating the reliability of PPI data; Investigate and develop effective methods for combining multiple, diverse and independent clustering results to improve the quality and robustness of identification; and Investigate and develop methods for integration of protein interaction data and gene expression profiles. This project will facilitate the development of novel educational tools and will involve both undergraduate and graduate students. Moreover, Florida International University (FIU) is among the top awarders of degrees to Hispanic students in US and this project will build on this strong history by promoting the participation of underrepresented groups in its research efforts.Additional information about the project can be found at http://www.cs.fiu.edu/~taoli.