Hub chacterization from sequence information using statistical methods

Maps of Protein Interaction Networks (PIN) are important as they provide clues about the functions of individual proteins as well as enable system level analyses of cellular processes. Predicting hub proteins, the highly connected proteins in PIN, is a challenging computational problem. This paper proposes two statistical methods for predicting hub proteins which applied on two different data bases - APID, HPRD - has revealed good results. In these methods, Shannon Index (a biodiversity measure) is used along with amino acid attributes Transfer Free Energy to Surface (TFES) and Hydrophobicity to distinguish hub proteins from non-hub proteins.

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