Prediction of protein subcellular localization based on primary sequence data

This paper describes a system called prediction of protein subcellular localization (P2SL) that predicts the subcellular localization of proteins in eukaryotic organisms based on the amino acid content of primary sequences using amino acid order. Our approach for prediction is to find the most frequent motifs for each protein (class) based on clustering and then to use these most frequent motifs as features for classification. This approach allows a classification independent of the length of the sequence. Another important property of the approach is to provide a means to perform reverse analysis and analysis to extract rules. In addition to these and more importantly, we describe the use of a new encoding scheme for the amino acids that conserves biological function based on point of accepted mutations (PAM) substitution matrix. We present preliminary results of our system on a two class (dichotomy) classifier. However, it can be extended to multiple classes with some modifications. © Springer-Verlag Berlin Heidelberg 2003

Prediction of protein subcellular localization based on primary sequence data [Birincil Dizi Veri Temelli Protein Hücre İçi Yer Belirleme Tahmini]

Subcellular localization is crucial for determining the functions of proteins. A system called prediction of protein subcellular localization (P2SL) that predicts the subcellular localization of proteins in eukaryotic organisms based on the amino acid content of primary sequences using amino acid order is designed. The approach for prediction is to find the most frequent motifs for each protein in a given class based on clustering via self organizing maps and then to use these most frequent motifs as features for classification by the help of multi layer perceptrons. This approach allows a classification independent of the length of the sequence. In addition to these, the use of a new encoding scheme is described for the amino acids that conserves biological function based on point of accepted mutations (PAM) substitution matrix. The statistical test results of the system is presented on a four class problem. P2SL achieves slightly higher prediction accuracy than the similar studies. © 2004 IEEE