Roles of toll-like receptors in cancer: a double-edged sword for defense and offense S Basith, B Manavalan, TH Yoo, SG Kim, S Choi Archives of pharmacal research 35, 1297-1316, 2012 | 266 | 2012 |
Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening S Basith, B Manavalan, T Hwan Shin, G Lee Medicinal research reviews 40 (4), 1276-1314, 2020 | 263 | 2020 |
mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation B Manavalan, S Basith, TH Shin, L Wei, G Lee Bioinformatics 35 (16), 2757-2765, 2019 | 233 | 2019 |
MLACP: machine-learning-based prediction of anticancer peptides B Manavalan, S Basith, TH Shin, S Choi, MO Kim, G Lee Oncotarget 8 (44), 77121, 2017 | 233 | 2017 |
Meta-4mCpred: a sequence-based meta-predictor for accurate DNA 4mC site prediction using effective feature representation B Manavalan, S Basith, TH Shin, L Wei, G Lee Molecular Therapy-Nucleic Acids 16, 733-744, 2019 | 207 | 2019 |
AIPpred: sequence-based prediction of anti-inflammatory peptides using random forest B Manavalan, TH Shin, MO Kim, G Lee Frontiers in pharmacology 9, 276, 2018 | 196 | 2018 |
Machine-learning-based prediction of cell-penetrating peptides and their uptake efficiency with improved accuracy B Manavalan, S Subramaniyam, TH Shin, MO Kim, G Lee Journal of proteome research 17 (8), 2715-2726, 2018 | 189 | 2018 |
HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation MM Hasan, N Schaduangrat, S Basith, G Lee, W Shoombuatong, ... Bioinformatics 36 (11), 3350-3356, 2020 | 180 | 2020 |
PVP-SVM: sequence-based prediction of phage virion proteins using a support vector machine B Manavalan, TH Shin, G Lee Frontiers in microbiology 9, 476, 2018 | 177 | 2018 |
mACPpred: a support vector machine-based meta-predictor for identification of anticancer peptides V Boopathi, S Subramaniyam, A Malik, G Lee, B Manavalan, DC Yang International journal of molecular sciences 20 (8), 1964, 2019 | 175 | 2019 |
iBCE-EL: a new ensemble learning framework for improved linear B-cell epitope prediction B Manavalan, RG Govindaraj, TH Shin, MO Kim, G Lee Frontiers in immunology 9, 1695, 2018 | 174 | 2018 |
SDM6A: a web-based integrative machine-learning framework for predicting 6mA sites in the rice genome S Basith, B Manavalan, TH Shin, G Lee Molecular Therapy-Nucleic Acids 18, 131-141, 2019 | 151 | 2019 |
Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools R Su, J Hu, Q Zou, B Manavalan, L Wei Briefings in bioinformatics 21 (2), 408-420, 2020 | 149 | 2020 |
Toll-like receptor modulators: a patent review (2006–2010) S Basith, B Manavalan, G Lee, SG Kim, S Choi Expert opinion on therapeutic patents 21 (6), 927-944, 2011 | 148 | 2011 |
SVMQA: support–vector-machine-based protein single-model quality assessment B Manavalan, J Lee Bioinformatics 33 (16), 2496-2503, 2017 | 145 | 2017 |
BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides P Charoenkwan, C Nantasenamat, MM Hasan, B Manavalan, ... Bioinformatics 37 (17), 2556-2562, 2021 | 144 | 2021 |
Metabolome changes in cerebral ischemia TH Shin, DY Lee, S Basith, B Manavalan, MJ Paik, I Rybinnik, ... Cells 9 (7), 1630, 2020 | 134 | 2020 |
Iterative feature representations improve N4-methylcytosine site prediction L Wei, R Su, S Luan, Z Liao, B Manavalan, Q Zou, X Shi Bioinformatics 35 (23), 4930-4937, 2019 | 133 | 2019 |
Similar structures but different roles–an updated perspective on TLR structures B Manavalan, S Basith, S Choi Frontiers in physiology 2, 41, 2011 | 132 | 2011 |
Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine … MM Hasan, S Basith, MS Khatun, G Lee, B Manavalan, H Kurata Briefings in Bioinformatics 22 (3), bbaa202, 2021 | 119 | 2021 |