Predicting protein subcellular location: exploiting amino acid based sequence of feature spaces and fusion of diverse classifiers.
A novel approach CE-Ploc is proposed for predicting protein subcellular locations by exploiting diversity both in feature and decision spaces. The diversity in a sequence of feature spaces is exploited using hydrophobicity and hydrophilicity of amphiphilic pseudo amino acid composition and a specific learning mechanism. Diversity in learning mechanisms is exploited by fusion of classifiers that are based on different learning mechanisms. Significant improvement in prediction performance is observed using jackknife and independent dataset tests.
Khan
Asifullah
A
Department of Mechatronics, Gwangju Institute of Science and Technology, Buk-Gu, Gwangju, 500-712, Republic of Korea. asifullah@gist.ac.kr
Majid
Abdul
A
Choi
Tae-Sun
TS
D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D057927:Hydrophobic and Hydrophilic Interactions; D021381:Protein Transport; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
D011506:Proteins