| A comprehensive genome‐scale reconstruction of Escherichia coli metabolism—2011 JD Orth, TM Conrad, J Na, JA Lerman, H Nam, AM Feist, BØ Palsson Molecular systems biology 7 (1), 535, 2011 | 1282 | 2011 |
| DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences I Lee, J Keum, H Nam PLoS computational biology 15 (6), e1007129, 2019 | 407 | 2019 |
| Network context and selection in the evolution to enzyme specificity H Nam, NE Lewis, JA Lerman, DH Lee, RL Chang, D Kim, BO Palsson Science 337 (6098), 1101-1104, 2012 | 308 | 2012 |
| The CH25H–CYP7B1–RORα axis of cholesterol metabolism regulates osteoarthritis WS Choi, G Lee, WH Song, JT Koh, J Yang, JS Kwak, HE Kim, SK Kim, ... Nature 566 (7743), 254-258, 2019 | 229 | 2019 |
| Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification H Nam, BC Chung, Y Kim, KY Lee, D Lee Bioinformatics 25 (23), 3151-3157, 2009 | 135 | 2009 |
| Discovering health benefits of phytochemicals with integrated analysis of the molecular network, chemical properties and ethnopharmacological evidence S Yoo, K Kim, H Nam, D Lee Nutrients 10 (8), 1042, 2018 | 98 | 2018 |
| Cross-species oncogenic signatures of breast cancer in canine mammary tumors TM Kim, IS Yang, BJ Seung, S Lee, D Kim, YJ Ha, M Seo, KK Kim, HS Kim, ... Nature communications 11 (1), 3616, 2020 | 85 | 2020 |
| Virmid: accurate detection of somatic mutations with sample impurity inference S Kim, K Jeong, K Bhutani, JH Lee, A Patel, E Scott, H Nam, H Lee, ... Genome biology 14, 1-17, 2013 | 85 | 2013 |
| Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches H Kim, E Kim, I Lee, B Bae, M Park, H Nam Biotechnology and Bioprocess Engineering 25, 895-930, 2020 | 83 | 2020 |
| Systems assessment of transcriptional regulation on central carbon metabolism by Cra and CRP D Kim, SW Seo, Y Gao, H Nam, GI Guzman, BK Cho, BO Palsson Nucleic acids research 46 (6), 2901-2917, 2018 | 81 | 2018 |
| A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks H Nam, M Campodonico, A Bordbar, DR Hyduke, S Kim, DC Zielinski, ... PLoS computational biology 10 (9), e1003837, 2014 | 80 | 2014 |
| SELF-BLM: Prediction of drug-target interactions via self-training SVM J Keum, H Nam PloS one 12 (2), e0171839, 2017 | 74 | 2017 |
| Drug repositioning of herbal compounds via a machine-learning approach E Kim, A Choi, H Nam BMC bioinformatics 20, 33-43, 2019 | 66 | 2019 |
| The use of technical replication for detection of low-level somatic mutations in next-generation sequencing J Kim, D Kim, JS Lim, JH Maeng, H Son, HC Kang, H Nam, JH Lee, S Kim Nature communications 10 (1), 1047, 2019 | 61 | 2019 |
| Identification of drug-target interaction by a random walk with restart method on an interactome network I Lee, H Nam BMC bioinformatics 19, 9-18, 2018 | 56 | 2018 |
| Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints E Kim, H Nam BMC bioinformatics 18, 25-34, 2017 | 48 | 2017 |
| Predicting the absorption potential of chemical compounds through a deep learning approach M Shin, D Jang, H Nam, KH Lee, D Lee IEEE/ACM transactions on computational biology and bioinformatics 15 (2 …, 2016 | 48 | 2016 |
| Identification of temporal association rules from time-series microarray data sets H Nam, KY Lee, D Lee BMC bioinformatics 10, 1-9, 2009 | 48 | 2009 |
| hERG-Att: Self-attention-based deep neural network for predicting hERG blockers H Kim, H Nam Computational Biology and Chemistry 87, 107286, 2020 | 42 | 2020 |
| Sequence-based prediction of protein binding regions and drug–target interactions I Lee, H Nam Journal of cheminformatics 14 (1), 5, 2022 | 35 | 2022 |
Doheon LeeProfessor of Bio and Brain Engineering, KAIST在 kaist.ac.kr 的电子邮件经过验证
