| 2022 | | Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2 | Balachandran, Manavalan, Basith, Shaherin, 이광 |
| 2019 | | Comparison between comorbidity adjustment tools in the prediction model for hospital mortalit | 백설경 |
| 2021 | | Computational prediction of species-specific yeast DNA replication origin via iterative feature representation | Balachandran, Manavalan, Basith, Shaherin, 신태환, 이광 |
| 2021 | | Critical evaluation of web-based DNA N6-methyladenine site prediction tools | Balachandran, Manavalan |
| 2022 | | Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics | 신태환, 이광 |
| 2018 | | DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest | Balachandran, Manavalan, 이광 |
| 2020 | | Empirical Comparison and Analysis of Web-Based DNA N (4)-Methylcytosine Site Prediction Tools | Balachandran, Manavalan, Basith, Shaherin, 신태환, 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 |
| 2023 | | Intrinsic and Extrinsic Transcriptional Profiles That Affect the Clinical Response to PD-1 Inhibitors in Patients with Non–Small Cell Lung Cancer | 고영화, 변혜은, 이현우, 한재호, 함석진 |
| 2020 | | Machine intelligence in peptide therapeutics: A next-generation tool for rapid disease screening | Balachandran, Manavalan, Basith, Shaherin, 신태환, 이광 |
| 2022 | | Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies | 김미란, 장혜진 |
| 2023 | | Machine learning-based prediction model for postoperative delirium in non-cardiac surgery | 박정찬 |
| 2021 | | Machine-learning model to predict the cause of death using a stacking ensemble method for observational data | 박래웅, 정재연 |
| 2018 | | Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy | Balachandran, Manavalan, 신태환, 이광 |
| 2021 | | Mortality Risk Factors in Myocardial Injury after Noncardiac Surgery: Registry extracted via Clinical Data Warehouse (CDW) | 양광모 |
| 2021 | | NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning | Balachandran, Manavalan |
| 2018 | | PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions | Balachandran, Manavalan, 이광 |
| 2022 | | Prediction Model for 30-Day Mortality after Non-Cardiac Surgery Using Machine-Learning Techniques Based on Preoperative Evaluation of Electronic Medical Records | 김하연, 박래웅 |
| 2023 | | Prediction model for postoperative atrial fibrillation in non-cardiac surgery using machine learning | 김하연 |
| 2024 | | Prediction Models for Readmission Using Home Healthcare Notes and OMOP-CDM | 박래웅 |
| 2020 | | Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases | 박래웅, 손상준, 진우람 |
| 2018 | | PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine | Balachandran, Manavalan, 이광 |
| 2019 | | SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome | Balachandran, Manavalan, Basith, Shaherin, 신태환, 이광 |
| 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 |
