Issue No. 06 - Nov.-Dec. (2018 vol. 15)
Hamid D. Ismail , Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC
Hiroto Saigo , Kyushu University, Fukuoka, Japan
Dukka B KC , Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC
The Nuclear Receptor (NR) superfamily plays an important role in key biological, developmental, and physiological processes. Developing a method for the classification of NR proteins is an important step towards understanding the structure and functions of the newly discovered NR protein. The recent studies on NR classification are either unable to achieve optimum accuracy or are not designed for all the known NR subfamilies. In this study, we developed RF-NR, which is a Random Forest based approach for improved classification of nuclear receptors. The RF-NR can predict whether a query protein sequence belongs to one of the eight NR subfamilies or it is a non-NR sequence. The RF-NR uses spectrum-like features namely: Amino Acid Composition, Di-peptide Composition, and Tripeptide Composition. Benchmarking on two independent datasets with varying sequence redundancy reduction criteria, the RF-NR achieves better (or comparable) accuracy than other existing methods. The added advantage of our approach is that we can also obtain biological insights about the important features that are required to classify NR subfamilies. RF-NR is freely available at
Amino acids, Benchmark testing, Protein sequence, Feature extraction, Kernel
H. D. Ismail, H. Saigo and D. B. KC, "RF-NR: Random Forest Based Approach for Improved Classification of Nuclear Receptors," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 15, no. 6, pp. 1844-1852, 2018.