Browsing by Keyword : Algorithms
Showing results 61 to 73 of 73
Pub Year | | Title | Author(s) |
2012 | | Prioritization of SNPs for genome-wide association studies using an interaction model of genetic variation, gene expression, and trait variation | 백효정 |
2023 | | Prognostic artificial intelligence model to predict 5 year survival at 1 year after gastric cancer surgery based on nutrition and body morphometry | 한상욱, 허지미, 허훈 |
2021 | | Right bundle branch block–type wide QRS complex tachycardia with a reversed R/S complex in lead V6: Development and validation of electrocardiographic differentiation criteria | 이광노 |
2021 | | StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides | Balachandran, Manavalan |
2017 | | Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records | 박래웅, 윤덕용 |
2020 | | SWATH-MS analysis of cerebrospinal fluid to generate a robust battery of biomarkers for Alzheimer's disease | 박범희, 박선아 |
2017 | | The effect of continuous positive airway pressure on cardiopulmonary coupling | 김현준 |
2012 | | The importance of morphometric radiographic vertebral assessment for the detection of patients who need pharmacological treatment of osteoporosis among postmenopausal diabetic Korean women | 정윤석, 최용준 |
2016 | | Trabecular Bone Score (TBS) and TBS-Adjusted Fracture Risk Assessment Tool are Potential Supplementary Tools for the Discrimination of Morphometric Vertebral Fractures in Postmenopausal Women With Type 2 Diabetes | 정윤석, 최용준 |
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 |
2006 | | Underlying mechanism for NMDA receptor antagonism by the anti-inflammatory drug, sulfasalazine, in mouse cortical neurons. | 곽병주 |
2021 | | Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder | 임홍석 |