ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins

Background: The expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features. Results: Here, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM). In addition, recently availabl...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Aarti Garg, Gajendra P. S. Raghava
• Keywords: BMCBI 2008, N-terminal Sequence Composition, Protein, Subcellular Localization