Browsing by Keyword : Machine Learning

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Showing results 22 to 50 of 50

Pub YearTitleAuthor(s)
2021Factors to improve distress and fatigue in Cancer survivorship; further understanding through text analysis of interviews by machine learning김지나, 안미선, 전미선
2023Feasibility Study of Federated Learning on the Distributed Research Network of OMOP Common Data Model박래웅
2020HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representationBalachandran, Manavalan, Basith, Shaherin, 이광
2020i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genomeBalachandran, Manavalan
2020i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representationBalachandran, Manavalan
2021Integrative machine learning framework for the identification of cell-specific enhancers from the human genomeBalachandran, Manavalan, Basith, Shaherin, 이광
2019Iterative feature representations improve N4-methylcytosine site predictionBalachandran, Manavalan
2018Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation최소연
2020Machine 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 study최소연
2018Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals박래웅, 윤덕용, 최영
2022Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies김미란, 장혜진
2023Machine learning-based prediction model for postoperative delirium in non-cardiac surgery김하연, 박래웅
2021Machine-learning model to predict the cause of death using a stacking ensemble method for observational data박래웅, 정재연
2018Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved AccuracyBalachandran, Manavalan, 신태환, 이광
2019mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representationBalachandran, Manavalan, Basith, Shaherin, 신태환, 이광
2021Meta-i6mA: An interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning frameworkBalachandran, Manavalan, Basith, Shaherin, 이광
2021MicroRNA signatures associated with lymph node metastasis in intramucosal gastric cancer김석휘, 배원정, 이다근
2021NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learningBalachandran, Manavalan
2021New approach of prediction of recurrence in thyroid cancer patients using machine learning김수영
2021Predicting speech discrimination scores from pure-tone thresholds—A machine learning-based approach using data from 12,697 subjects김한태, 장정훈, 정연훈
2019Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data문소영
2023Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study박래웅, 손상준
2022Recent Trends on the Development of Machine Learning Approaches for the Prediction of Lysine Acetylation SitesBasith, Shaherin, 이광, 장혜진
2021StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptidesBalachandran, Manavalan
2022STALLION: A stacking-based ensemble learning framework for prokaryotic lysine acetylation site predictionBalachandran, Manavalan, Basith, Shaherin, 이광
2022The Bio-signal based Model to Predict the Occurrence of Delirium in Intensive Care Unit(ICU)김형준
2022THRONE: A New Approach for Accurate Prediction of Human RNA N7-Methylguanosine SitesBasith, Shaherin, 이광
2023Translation of Machine Learning-Based Prediction Algorithms to Personalised Empiric Antibiotic Selection: A Population-Based Cohort Study박래웅, 최영화
2021Umpred-frl: A new approach for accurate prediction of umami peptides using feature representation learningBalachandran, Manavalan
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