| 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 | 159 | 2020 |
| ACPred: a computational tool for the prediction and analysis of anticancer peptides N Schaduangrat, C Nantasenamat, V Prachayasittikul, W Shoombuatong Molecules 24 (10), 1973, 2019 | 139 | 2019 |
| Unraveling the bioactivity of anticancer peptides as deduced from machine learning W Shoombuatong, N Schaduangrat, C Nantasenamat EXCLI journal 17, 734, 2018 | 111 | 2018 |
| 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 | 102 | 2021 |
| Meta-iAVP: a sequence-based meta-predictor for improving the prediction of antiviral peptides using effective feature representation N Schaduangrat, C Nantasenamat, V Prachayasittikul, W Shoombuatong International journal of molecular sciences 20 (22), 5743, 2019 | 93 | 2019 |
| StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides P Charoenkwan, W Chiangjong, C Nantasenamat, MM Hasan, ... Briefings in bioinformatics 22 (6), bbab172, 2021 | 92 | 2021 |
| iUmami-SCM: a novel sequence-based predictor for prediction and analysis of umami peptides using a scoring card method with propensity scores of dipeptides P Charoenkwan, J Yana, C Nantasenamat, MM Hasan, W Shoombuatong Journal of Chemical Information and Modeling 60 (12), 6666-6678, 2020 | 85 | 2020 |
| HemoPred: a web server for predicting the hemolytic activity of peptides TS Win, AA Malik, V Prachayasittikul, JE S Wikberg, C Nantasenamat, ... Future medicinal chemistry 9 (3), 275-291, 2017 | 85 | 2017 |
| Computer-aided drug design of bioactive natural products V Prachayasittikul, A Worachartcheewan, W Shoombuatong, ... Current Topics in Medicinal Chemistry 15 (18), 1780-1800, 2015 | 84 | 2015 |
| iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides P Charoenkwan, J Yana, N Schaduangrat, C Nantasenamat, MM Hasan, ... Genomics 112 (4), 2813-2822, 2020 | 83 | 2020 |
| SCMCRYS: predicting protein crystallization using an ensemble scoring card method with estimating propensity scores of P-collocated amino acid pairs P Charoenkwan, W Shoombuatong, HC Lee, J Chaijaruwanich, ... PloS one 8 (9), e72368, 2013 | 82 | 2013 |
| i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation MM Hasan, B Manavalan, W Shoombuatong, MS Khatun, H Kurata Plant molecular biology 103, 225-234, 2020 | 69 | 2020 |
| iDPPIV-SCM: a sequence-based predictor for identifying and analyzing dipeptidyl peptidase IV (DPP-IV) inhibitory peptides using a scoring card method P Charoenkwan, S Kanthawong, C Nantasenamat, MM Hasan, ... Journal of proteome research 19 (10), 4125-4136, 2020 | 68 | 2020 |
| Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking S Simeon, N Anuwongcharoen, W Shoombuatong, AA Malik, ... PeerJ 4, e2322, 2016 | 64 | 2016 |
| iQSP: a sequence-based tool for the prediction and analysis of quorum sensing peptides using informative physicochemical properties P Charoenkwan, N Schaduangrat, C Nantasenamat, T Piacham, ... International Journal of Molecular Sciences 21 (1), 75, 2019 | 61 | 2019 |
| Predicting metabolic syndrome using the random forest method A Worachartcheewan, W Shoombuatong, P Pidetcha, W Nopnithipat, ... The Scientific World Journal 2015, 2015 | 61 | 2015 |
| i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes MM Hasan, B Manavalan, W Shoombuatong, MS Khatun, H Kurata Computational and structural biotechnology journal 18, 906-912, 2020 | 60 | 2020 |
| THPep: a machine learning-based approach for predicting tumor homing peptides W Shoombuatong, N Schaduangrat, R Pratiwi, C Nantasenamat Computational biology and chemistry 80, 441-451, 2019 | 59 | 2019 |
| NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning MM Hasan, MA Alam, W Shoombuatong, HW Deng, B Manavalan, ... Briefings in Bioinformatics 22 (6), bbab167, 2021 | 57 | 2021 |
| Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation P Charoenkwan, C Nantasenamat, MM Hasan, W Shoombuatong Journal of Computer-Aided Molecular Design 34 (10), 1105-1116, 2020 | 57 | 2020 |
