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Technical Report CMS-TR-20130821


Title Hybrid feature selection and peptide binding affinity prediction using an EDA based algorithm
Author/s Kalpesh Shelke
Centre for Modeling and Simulation, Savitribai Phule Pune University, Pune 411 007 India


Shrikant Jayraman
Centre for Modeling and Simulation, Savitribai Phule Pune University, Pune 411 007 India


Shameek Ghosh
Centre for Development of Advanced Computing (C-DAC), Pune 411007 India


V.K. Jayaraman
Centre for Development of Advanced Computing (C-DAC), Pune 411007 India
Abstract Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. The process of drug discovery often involves the use of quantitative structure-activity relationship (QSAR) models to identify chemical structures that could have good inhibitory effects on specific targets and have low toxicity (non-specific activity). QSAR models are regression or classification models used in the chemical and biological sciences. Because of high dimensionality problems, a feature selection problem is imminent. In this study, we thus employ a hybrid Estimation of Distribution Algorithm (EDA) based filter-wrapper methodology to simultaneously extract informative feature subsets and build robust QSAR models. The performance of the algorithm was tested on the benchmark classification challenge datasets obtained from the CoePRa competition platform, developed in 2006. Our results clearly demonstrate the efficacy of a hybrid EDA filter-wrapper algorithm in comparison to the results reported earlier.
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Citing This Document Kalpesh Shelke, Shrikant Jayaraman, Shameek Ghosh, and V.K. Jayaraman , Hybrid feature selection and peptide binding affinity prediction using an EDA based algorithm . Technical Report CMS-TR-20130821 of the Centre for Modeling and Simulation, Savitribai Phule Pune University, Pune 411007, India (2013); available at http://cms.unipune.ac.in/reports/.
Notes, Published Reference, Etc. Published in 2013 IEEE Congress on Evolutionary Computation (CEC)
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