Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics

Abstract— Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics. Mass spectrometry technique is gradually gaining momentum among the recent techniques deployed by several analytical research labs which intends to study biological or chemical properties of complex structures such as protein sequences. Literature reveals that reasoning voluminous mass spectrometry data via sophisticated computational techniques inspired by observing natural processes adapted by biological life has been yielding fruitful results towards the advancement of fields including bioinformatics and proteomics. < Final Year Projects > Such advanced approaches provide efficient ways to mine mass spectrometry data in order to extract discriminating features that aid in discovering vital information, specifically discovering disease-related protein patterns in complex protein sequences. This study reveals the use of artificial bee colony (ABC) as a new feature selection technique incorporated with SVM classifier. Results achieved 96 and 100% for sensitivity and specificity respectively in discriminating cirrhosis and liver cancer cases.