| 2018 | | Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation | 최소연 |
| 2020 | | Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study | 최소연 |
| 2018 | | Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals | 박래웅, 윤덕용, 최영 |
| 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 | 김하연, 박래웅 |
| 2024 | | Machine learning-driven prediction of brain metastasis in lung adenocarcinoma using miRNA profile and target gene pathway analysis of an mRNA dataset | 고영화, 이현우, 한재호, 함석진 |
| 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 |
| 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 | | MicroRNA signatures associated with lymph node metastasis in intramucosal gastric cancer | 김석휘, 배원정, 이다근 |
| 2024 | | Multimodal Model for Predicting Fetal Acidosis in Delivery Room | 김미란, 염선형, 장혜진, 조은애, 황경주 |
| 2024 | | Neuroimaging and natural language processing-based classification of suicidal thoughts in major depressive disorder | 박래웅, 박범희, 손상준 |
| 2021 | | NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning | Balachandran, Manavalan |
| 2021 | | New approach of prediction of recurrence in thyroid cancer patients using machine learning | 김수영 |
| 2021 | | Predicting speech discrimination scores from pure-tone thresholds—A machine learning-based approach using data from 12,697 subjects | 김한태, 장정훈, 정연훈 |
| 2019 | | Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data | 문소영 |
| 2023 | | Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study | 박래웅, 손상준 |
| 2022 | | Recent Trends on the Development of Machine Learning Approaches for the Prediction of Lysine Acetylation Sites | Basith, Shaherin, 이광, 장혜진 |
| 2024 | | SEP-AlgPro: An efficient allergen prediction tool utilizing traditional machine learning and deep learning techniques with protein language model features | Basith, Shaherin, 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 | | The Bio-signal based Model to Predict the Occurrence of Delirium in Intensive Care Unit(ICU) | 김형준 |
| 2022 | | THRONE: A New Approach for Accurate Prediction of Human RNA N7-Methylguanosine Sites | Basith, Shaherin, 이광 |
| 2023 | | Translation of Machine Learning-Based Prediction Algorithms to Personalised Empiric Antibiotic Selection: A Population-Based Cohort Study | 박래웅, 최영화 |
| 2021 | | Umpred-frl: A new approach for accurate prediction of umami peptides using feature representation learning | Balachandran, Manavalan |
