Hojung Nam
Hojung Nam
Associate Professor at Gwangju Institute of Science and Technology (GIST)
Dirección de correo verificada de gist.ac.kr
Citado por
Citado por
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
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
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
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
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), 1-17, 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
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
Self-blm: Prediction of drug-target interactions via self-training svm
J Keum, H Nam
PloS one 12 (2), e0171839, 2017
Identification of temporal association rules from time-series microarray data sets
H Nam, KY Lee, D Lee
BMC bioinformatics 10 (3), 1-9, 2009
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
Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints
E Kim, H Nam
BMC bioinformatics 18 (7), 25-34, 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), 9-18, 2018
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
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
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
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), 417-425, 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
Drug repositioning of herbal compounds via a machine-learning approach
E Kim, A Choi, H Nam
BMC bioinformatics 20 (10), 33-43, 2019
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
Prognostic factor analysis for breast cancer using gene expression profiles
S Joe, H Nam
BMC medical informatics and decision making 16 (1), 11-19, 2016
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Artículos 1–20