| 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 |
| 2021 | | A blood-based transcriptomic signature for noninvasive diagnosis of gastric cancer | 허훈 |
| 2022 | | A Machine Learning Approach Using PET/CT-based Radiomics for Prediction of PD-L1 Expression in Non-small Cell Lung Cancer | 고영화, 이수진 |
| 2019 | | A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer | 고영화, 안미선, 이수진 |
| 2024 | | A multimodal machine learning model for predicting dementia conversion in Alzheimer’s disease | 손상준 |
| 2021 | | A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data | 박래웅 |
| 2022 | | Accelerating bioactive peptide discovery via mutual information-based meta-learning | Balachandran, Manavalan |
| 2020 | | Analysis of Adverse Drug Reactions Identified in Nursing Notes Using Reinforcement Learning | 박래웅 |
| 2019 | | Angiography-Based Machine Learning for Predicting Fractional Flow Reserve in Intermediate Coronary Artery Lesions | 최소연 |
| 2024 | | Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables | 박래웅, 박인휘, 신규태, 이민정, 이윤지 |
| 2024 | | Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables | 박인휘, 이윤지 |
| 2022 | | Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review | 김정택 |
| 2023 | | Association between impaired glucose metabolism and long-term prognosis at the time of diagnosis of depression: Impaired glucose metabolism as a promising biomarker proposed through a machine-learning approach | 노재성, 박래웅, 손상준, 조용혁 |
| 2021 | | Association of TLR 9 gene polymorphisms with remission in patients with rheumatoid arthritis receiving TNF-α inhibitors and development of machine learning models | 정주양 |
| 2009 | | Basic Concepts and Principles of Data Mining in Clinical Practice | 박래웅 |
| 2017 | | Cascade recurring deep networks for audible range prediction | 정연훈, 추옥성 |
| 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 |
| 2023 | | Development of Clinical Information Extraction Model from Unstructured Clinical Reports using Natural Language Processing | 박래웅, 박지명 |
| 2021 | | Effectiveness of transfer learning for deep learning-based electrocardiogram analysis | 윤덕용 |
| 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 | | Efficacy of automated machine learning models and feature engineering for diagnosis of equivocal appendicitis using clinical and computed tomography findings | 안정환, 안주호 |
| 2020 | | Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools | Balachandran, Manavalan |
| 2024 | | Evaluation of machine learning approach for surgical results of Ahmed valve implantation in patients with glaucoma | 안재홍, 이승엽 |
| 2016 | | Evaluation of Underlying Lymphocytic Thyroiditis With Histogram Analysis Using Grayscale Ultrasound Images | 하은주 |
| 2020 | | Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides | Balachandran, Manavalan, Basith, Shaherin, 신태환, 이광, 이다연 |
| 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 |
| 2023 | | Feasibility Study of Federated Learning on the Distributed Research Network of OMOP Common Data Model | 박래웅 |
| 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 |
| 2024 | | Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network | 노현웅, 손상준, 우현구, 홍창형 |
| 2021 | | Integrative machine learning framework for the identification of cell-specific enhancers from the human genome | Balachandran, Manavalan, Basith, Shaherin, 이광 |
| 2024 | | Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model | 박래웅 |
| 2019 | | Iterative feature representations improve N4-methylcytosine site prediction | Balachandran, Manavalan |
| 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 |
