Predicts the subcellular locations of multi-label gram-negative bacterial proteins. Gneg-ECC-mPLoc extracts Gene Ontology (GO) feature vectors from GO terms of homologs of query proteins and then adopts a powerful multi-label ensemble classifier to output the final multi-label prediction results. Gneg-ECC-mPLoc can efficiently improve prediction accuracy of subcellular localization of gram-negative bacterial proteins.
School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China; The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Department of Control Science and Engineering, Tongji University, Shanghai, China; Information Management Center, Training Department, Beijing Armed Police Command College, Beijing, China
Gneg-ECC-mPLoc funding source(s)
This work was supported by National Natural Science Foundation of China (61402422 and 61273305), Key Project of Science and Technology Research of the Education Department of Henan Province (14A520063), Doctoral Research Fund of Zhengzhou University of Light Industry (2013BSJJ082) and Open Fund of MOE Key Laboratory of Embedded System and Service Computing of Tongji University (ESSCKF201308).