| 2019 | | 4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N(4)-methylcytosine Sites in the Mouse Genome | Balachandran, Manavalan, Basith, Shaherin, 신태환, 이광 |
| 2022 | | Accelerating bioactive peptide discovery via mutual information-based meta-learning | Balachandran, Manavalan |
| 2022 | | Cerebrospinal Fluid Metabolome in Parkinson’s Disease and Multiple System Atrophy | 신태환, 이광 |
| 2022 | | Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2 | Balachandran, Manavalan, Basith, Shaherin, 이광 |
| 2020 | | Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools | Balachandran, Manavalan |
| 2020 | | Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides | Balachandran, Manavalan, Basith, Shaherin, 신태환, 이광, 이다연 |
| 2020 | | HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation | Balachandran, Manavalan, Basith, Shaherin, 이광 |
| 2020 | | i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome | Balachandran, Manavalan |
| 2020 | | i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation | Balachandran, Manavalan |
| 2021 | | Integrative machine learning framework for the identification of cell-specific enhancers from the human genome | Balachandran, Manavalan, Basith, Shaherin, 이광 |
| 2019 | | Iterative feature representations improve N4-methylcytosine site prediction | Balachandran, Manavalan |
| 2018 | | Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy | Balachandran, Manavalan, 신태환, 이광 |
| 2019 | | mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation | Balachandran, Manavalan, Basith, Shaherin, 신태환, 이광 |
| 2021 | | Meta-i6mA: An interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework | Balachandran, Manavalan, Basith, Shaherin, 이광 |
| 2021 | | NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning | Balachandran, Manavalan |
| 2022 | | Recent Trends on the Development of Machine Learning Approaches for the Prediction of Lysine Acetylation Sites | Basith, Shaherin, 이광, 장혜진 |
| 2021 | | StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides | Balachandran, Manavalan |
| 2022 | | STALLION: A stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction | Balachandran, Manavalan, Basith, Shaherin, 이광 |
| 2022 | | THRONE: A New Approach for Accurate Prediction of Human RNA N7-Methylguanosine Sites | Basith, Shaherin, 이광 |
| 2021 | | Umpred-frl: A new approach for accurate prediction of umami peptides using feature representation learning | Balachandran, Manavalan |
