title: Feature Fusion Based SVM Classifier for Protein Subcellular Localization Prediction.
abstract: For the importance of protein subcellular localization in different branches of life science and drug discovery, researchers have focused their attentions on protein subcellular localization prediction. Effective representation of features from protein sequences plays a most vital role in protein subcellular localization prediction specially in case of machine learning techniques. Single feature representation-like pseudo amino acid composition (PseAAC), physiochemical property models (PPM), and amino acid index distribution (AAID) contains insufficient information from protein sequences. To deal with such problems, we have proposed two feature fusion representations, AAIDPAAC and PPMPAAC, to work with Support Vector Machine classifiers, which fused PseAAC with PPM and AAID accordingly. We have evaluated the performance for both single and fused feature representation of a Gram-negative bacterial dataset. We have got at least 3% more actual accuracy by AAIDPAAC and 2% more locative accuracy by PPMPAAC than single feature representation.
authors.forename: Julia
authors.initials: J
authors.lastname: Rahman
authors.forename: Md Nazrul Islam
authors.initials: MN
authors.lastname: Mondal
authors.forename: Md Khaled Ben
authors.initials: MK
authors.lastname: Islam
authors.forename: Md Al Mehedi
authors.initials: MA
authors.lastname: Hasan
mesh_terms: D001426:Bacterial Proteins / Q000235:genetics / Q000378:metabolism*; D006090:Gram-Negative Bacteria / Q000235:genetics / Q000378:metabolism*; D008954:Models, Biological; D060388:Support Vector Machine
chemical_list: D001426:Bacterial Proteins