| 2023 | | A Machine Learning Approach Using [18F]FDG PET-Based Radiomics for Prediction of Tumor Grade and Prognosis in Pancreatic Neuroendocrine Tumor | 박용진 |
| 2021 | | A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data | 박래웅 |
| 2023 | | ADP-Fuse: A novel two-layer machine learning predictor to identify antidiabetic peptides and diabetes types using multiview information | Basith, Shaherin, 이광 |
| 2021 | | Analysis of nanotoxicity with integrated omics and mechanobiology | Basith, Shaherin, 모정순, 신태환, 이광 |
| 2022 | | Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review | 김정택 |
| 2023 | | Artificial Intelligence for Neurosurgery: Current State and Future Directions | 김상현, 노성현, 조평구 |
| 2021 | | Automated detection of acute myocardial infarction using asynchronous electrocardiogram signals-preview of implementing artificial intelligence with multichannel electrocardiographs obtained from smartwatches: Retrospective study | 임홍석 |
| 2021 | | Biosignal-based digital biomarkers for prediction of ventilator weaning success | 박광주, 박주헌, 박지은, 정우영, 정윤정 |
| 2022 | | Cerebrospinal Fluid Metabolome in Parkinson’s Disease and Multiple System Atrophy | 신태환, 이광 |
| 2022 | | Clinicoradiological Characteristics in the Differential Diagnosis of Follicular-Patterned Lesions of the Thyroid: A Multicenter Cohort Study | 이다현, 하은주, 한미란 |
| 2023 | | Development of a machine learning-based fine-grained risk stratification system for thyroid nodules using predefined clinicoradiological features | 이다현, 하은주 |
| 2023 | | Effect of Artificial Intelligence or Machine Learning on Prediction of Hip Fracture Risk: Systematic Review | 김정택 |
| 2022 | | Effects of RETN polymorphisms on treatment response in rheumatoid arthritis patients receiving TNF-α inhibitors and utilization of machine-learning algorithms | 김현아, 정주양 |
| 2024 | | Enhancing readmission prediction models by integrating insights from home healthcare notes: Retrospective cohort study | 박래웅 |
| 2024 | | Evaluation of machine learning approach for surgical results of Ahmed valve implantation in patients with glaucoma | 안재홍, 이승엽 |
| 2024 | | Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing | 박래웅, 박범희, 손상준 |
| 2024 | | Factors influencing psychological distress among breast cancer survivors using machine learning techniques | 박진희, 배선형, 전미선 |
| 2021 | | Factors to improve distress and fatigue in Cancer survivorship; further understanding through text analysis of interviews by machine learning | 김지나, 안미선, 전미선 |
| 2020 | | i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes | Balachandran, Manavalan |
| 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 |
| 2018 | | iGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised tree | Balachandran, Manavalan, Basith, Shaherin, 신태환, 이광 |
| 2022 | | Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality | 박래웅, 조재형 |
| 2023 | | Machine Learning-assisted Quantitative Mapping of Intracortical Axonal Plasticity Following a Focal Cortical Stroke in Rodents | 김병곤, 박범희 |
| 2019 | | Machine learning-based identification of hip arthroplasty designs | 김정택 |
| 2023 | | Machine learning-based prediction model for postoperative delirium in non-cardiac surgery | 김하연, 박래웅 |
| 2024 | | mHPpred: Accurate identification of peptide hormones using multi-view feature learning | Basith, Shaherin, 이광 |
| 2022 | | Oculomics for sarcopenia prediction: a machine learning approach toward predictive, preventive, and personalized medicine | 김범택 |
| 2023 | | Osteoporosis Feature Selection and Risk Prediction Model by Machine Learning Using a Cross-Sectional Database | 김정택 |
| 2021 | | Pparγ targets‐derived diagnostic and prognostic index for papillary thyroid cancer | 김수영, 김재형, 임수빈 |
| 2021 | | Predictability of mortality in patients with myocardial injury after noncardiac surgery based on perioperative factors via machine learning: Retrospective study | 박래웅 |
| 2023 | | Predicting Mechanical Complications After Adult Spinal Deformity Operation Using a Machine Learning Based on Modified Global Alignment and Proportion Scoring With Body Mass Index and Bone Mineral Density | 김상현, 노성현 |
| 2019 | | Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data | 문소영 |
| 2019 | | Protein-Carbohydrate Interactions | Balachandran, Manavalan |
| 2022 | | Recent Trends on the Development of Machine Learning Approaches for the Prediction of Lysine Acetylation Sites | Basith, Shaherin, 이광, 장혜진 |
| 2024 | | Reduced lysosomal activity and increased amyloid beta accumulation in silica-coated magnetic nanoparticles-treated microglia | 이광 |
| 2022 | | SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids | Balachandran, Manavalan |
| 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 | | Silica-coated magnetic-nanoparticle-induced cytotoxicity is reduced in microglia by glutathione and citrate identified using integrated omics | Balachandran, Manavalan, Basith, Shaherin, 강엽, 김아영, 백은주, 신태환, 이광 |
| 2021 | | SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information | Balachandran, Manavalan |
| 2021 | | Tumor nonimmune-microenvironment-related gene expression signature predicts brain metastasis in lung adenocarcinoma patients after surgery: A machine learning approach using gene expression profiling | 고영화, 이현우, 한재호, 함석진 |
| 2021 | | Umpred-frl: A new approach for accurate prediction of umami peptides using feature representation learning | Balachandran, Manavalan |
