Efficient computational model for classification of protein localization images using Extended Threshold Adjacency Statistics and Support Vector Machines. (April 2018)

• Record Type: Journal Article
• Authors: Tahir, Muhammad; Jan, Bismillah; Hayat, Maqsood; Shah, Shakir Ullah; Amin, Muhammad
• Is Part Of: Computer methods and programs in biomedicine. Volume 157(2018)
• Page Start: 205
• Page End: 215
• Publication Date: 2018-04
• Subjects: Protein images -- Subcellular localization -- Threshold Adjacency Statistics -- Fluorescence microscopy -- Ensemble learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.01.021
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: ETAS-SubLoc prediction system is proposed for protein subcellular localization. Efficiently enhanced the discrimination power of Threshold Adjacency Statistics. ETAS transforms an image into seven binarized images using seven threshold ranges. Each feature space extracted from binarized images is used to train separate SVM. The combined predictions of all SVMs lead to improved prediction performance. Graphical abstract: Abstract: Background and objective: Discriminative and informative feature extraction is the core requirement for accurate and efficient classification of protein subcellular localization images so that drug development could be more effective. The objective of this paper is to propose a novel modification in the Threshold Adjacency Statistics technique and enhance its discriminative power. Methods: In this work, we utilized Threshold Adjacency Statistics from a novel perspective to enhance its discrimination power and efficiency. In this connection, we utilized seven threshold ranges to produce seven distinct feature spaces, which are then used to train seven SVMs. The final prediction is obtained through the majority voting scheme. The proposed ETAS-SubLoc system is tested on two benchmark datasets using 5-fold cross-validation technique. Results: We observed that our proposed novel utilization of TAS technique has improved the discriminative power of the classifier. The ETAS-SubLoc system has achieved 99.2% accuracy, 99.3% sensitivity and 99.1% specificity for Endogenous dataset outperforming the classical Threshold Adjacency Statistics technique. Similarly, 91.8% accuracy, 96.3% sensitivity and 91.6% specificity values are achieved for Transfected dataset. Conclusions: Simulation results validated the effectiveness of ETAS-SubLoc that provides superior prediction performance compared to the existing technique. The proposed methodology aims at providing support to pharmaceutical industry as well as research community towards better drug designing and innovation in the fields of bioinformatics and computational biology. The implementation code for replicating the experiments presented in this paper is available at:https://drive.google.com/file/d/0B7IyGPObWbSqRTRMcXI2bG5CZWs/view?usp=sharing