Protein subcellular localization of fluorescence imagery using spatial and transform domain features

MOTIVATION Subcellular localization of proteins is one of the most significant characteristics of living cells. Prediction of protein subcellular locations is crucial to the understanding of various protein functions. Therefore, an accurate, computationally efficient and reliable prediction system is required. RESULTS In this article, the predictions of various Support Vector Machine (SVM) models have been combined through majority voting. The proposed ensemble SVM-SubLoc has achieved the highest success rates of 99.7% using hybrid features of Haralick textures and local binary patterns (HarLBP), 99.4% using hybrid features of Haralick textures and Local Ternary Patterns (HarLTP). In addition, SVM-SubLoc has yielded 99.0% accuracy using only local ternary patterns (LTPs) based features. The dimensionality of HarLBP feature vector is 581 compared with 78 and 52 for HarLTP and LTPs, respectively. Hence, SVM-SubLoc in conjunction with LTPs is fast, sufficiently accurate and simple predictive system. The proposed SVM-SubLoc approach thus provides superior prediction performance using the reduced feature space compared with existing approaches. AVAILABILITY A web server accompanying the proposed prediction scheme is available at http://111.68.99.218/ SVM-SubLoc CONTACT [email protected]; [email protected] SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.