| 2022 | | Accelerating bioactive peptide discovery via mutual information-based meta-learning | Balachandran, Manavalan |
| 2022 | | CGV: Cancer Genome Viewer, a web service for integrative cancer genome and pharmacogenomic data analysis | 우현구, 최지혜 |
| 2022 | | Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2 | Balachandran, Manavalan, Basith, Shaherin, 이광 |
| 2020 | | Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides | Balachandran, Manavalan, Basith, Shaherin, 신태환, 이광, 이다연 |
| 2021 | | Integrative machine learning framework for the identification of cell-specific enhancers from the human genome | Balachandran, Manavalan, Basith, Shaherin, 이광 |
| 2019 | | mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides | Balachandran, Manavalan, 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 |
| 2010 | | Molecular modeling of the reductase domain to elucidate the reaction mechanism of reduction of peptidyl thioester into its corresponding alcohol in non-ribosomal peptide synthetases. | 이광 |
| 2021 | | NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning | Balachandran, Manavalan |
| 2019 | | Prediction of S-nitrosylation sites by integrating support vector machines and random forest | Balachandran, Manavalan |
| 2020 | | scTyper: a comprehensive pipeline for the cell typing analysis of single-cell RNA-seq data | 우현구, 최지혜 |
| 2019 | | SEQprocess: a modularized and customizable pipeline framework for NGS processing in R package | 우현구, 최지혜 |
| 2022 | | THRONE: A New Approach for Accurate Prediction of Human RNA N7-Methylguanosine Sites | Basith, Shaherin, 이광 |
