| A comprehensive genome‐scale reconstruction of Escherichia coli metabolism—2011 JD Orth, TM Conrad, J Na, JA Lerman, H Nam, AM Feist, BŲ Palsson Molecular systems biology 7 (1), 535, 2011 | 933 | 2011 |
| 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 | 235 | 2012 |
| 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 | 103 | 2009 |
| 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 (8), R90, 2013 | 60 | 2013 |
| 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 Comput Biol 10 (9), e1003837, 2014 | 48 | 2014 |
| Identification of temporal association rules from time-series microarray data sets H Nam, KY Lee, D Lee BMC bioinformatics 10 (S3), S6, 2009 | 38 | 2009 |
| 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 | 34 | 2019 |
| Self-blm: Prediction of drug-target interactions via self-training svm J Keum, H Nam PloS one 12 (2), e0171839, 2017 | 33 | 2017 |
| 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 | 27 | 2019 |
| The role of cellular objectives and selective pressures in metabolic pathway evolution H Nam, TM Conrad, NE Lewis Current opinion in biotechnology 22 (4), 595-600, 2011 | 26 | 2011 |
| 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 | 22 | 2018 |
| Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints E Kim, H Nam BMC bioinformatics 18 (7), 227, 2017 | 21 | 2017 |
| Identification of drug-target interaction by a random walk with restart method on an interactome network I Lee, H Nam BMC bioinformatics 19 (8), 208, 2018 | 20 | 2018 |
| Prediction of compound-target interactions of natural products using large-scale drug and protein information J Keum, S Yoo, D Lee, H Nam BMC bioinformatics 17 (6), 219, 2016 | 17 | 2016 |
| 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 | 16 | 2018 |
| 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 | 16 | 2016 |
| bZIPDB: a database of regulatory information for human bZIP transcription factors T Ryu, J Jung, S Lee, HJ Nam, SW Hong, JW Yoo, D Lee, D Lee BMC genomics 8 (1), 1-6, 2007 | 15 | 2007 |
| 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), 1-11, 2019 | 14 | 2019 |
| Computational identification of altered metabolism using gene expression and metabolic pathways H Nam, J Lee, D Lee Biotechnology and bioengineering 103 (4), 835-843, 2009 | 12 | 2009 |
| Drug repositioning of herbal compounds via a machine-learning approach E Kim, A Choi, H Nam BMC bioinformatics 20 (10), 33-43, 2019 | 9 | 2019 |
Doheon LeeProfessor of Bio and Brain Engineering, KAISTVerified email at kaist.ac.kr
