Mark Gerstein
Yale University

Computational Proteomics: Prediction of Protein Function on a Genome-Scale

My talk will address a major post-genomic challenge: trying to predict protein function on a genomic scale. I will approach both of this through analyzing the properties and attributes of proteins in a database framework. The work on predicting protein function will discuss the strengths and limitations of a number of approaches: (i) using sequence similarity; (ii) using structural similarity; (iii) clustering microarray experiments; and (iv) data integration. The last approach involves systematically combining information from the other three and holds the most promise for the future. For the sequence analysis, I will present a similarity threshold above which functional annotation can be transferred, and for the microarray analysis, I will present a new method of clustering expression timecourses that finds "time-shifted" relationships.

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