Sayamon HongjaiseeChanin NantasenamatTanawan Samleerat CarrawayWatshara ShoombuatongMahidol UniversityChiang Mai University2020-01-272020-01-272019-06-01Computational Biology and Chemistry. Vol.80, (2019), 419-432147692712-s2.0-85066120698https://repository.li.mahidol.ac.th/handle/20.500.14594/50159© 2019 Elsevier Ltd Determination of HIV-1 coreceptor usage is strongly recommended before starting the coreceptor-specific inhibitors for HIV treatment. Currently, the genotypic assays are the most interesting tools due to they are more feasible than phenotypic assays. However, most of prediction models were developed and validated by data set of HIV-1 subtype B and C. The present study aims to develop a powerful and reliable model to accurately predict HIV-1 coreceptor usage for CRF01_AE subtype called HIVCoR. HIVCoR utilized random forest and support vector machine as the prediction model, together with amino acid compositions, pseudo amino acid compositions and relative synonymous codon usage frequencies as the input feature. The overall success rate of 93.79% was achieved from the external validation test on the objective benchmark dataset. Comparison results indicated that HIVCoR was superior to other bioinformatics tools and genotypic predictors. For the convenience of experimental scientists, a user-friendly webserver has been established at http://codes.bio/hivcor/.Mahidol UniversityBiochemistry, Genetics and Molecular BiologyChemistryMathematicsArticleSCOPUS10.1016/j.compbiolchem.2019.05.006