Sequence-driven features for prediction of subcellular localization of proteins

✍ Scribed by Jong Kyoung Kim; Sung-Yang Bang; Seungjin Choi

• Publisher: Elsevier Science
• Year: 2006
• Tongue: English
• Weight: 241 KB
• Volume: 39
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/j.patcog.2006.02.021

✦ Synopsis

Prediction of the cellular location of a protein plays an important role in inferring the function of the protein. Feature extraction is a critical part in prediction systems, requiring raw sequence data to be transformed into appropriate numerical feature vectors while minimizing information loss. In this paper, we present a method for extracting useful features from protein sequence data. The method employs local and global pairwise sequence alignment scores as well as composition-based features. Five different features are used for training support vector machines (SVMs) separately and a weighted majority voting makes a final decision. The overall prediction accuracy evaluated by the 5-fold cross-validation reached 88.53% for the eukaryotic animal data set. Comparing the prediction accuracy of various feature extraction methods, provides a biological insight into the location of targeting information. Our experimental results confirm that our feature extraction methods are very useful for predicting subcellular localization of proteins.