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Hojung Nam
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2020 – today
- 2023
- [j21]Bongsung Bae, Haelee Bae, Hojung Nam:
LOGICS: Learning optimal generative distribution for designing de novo chemical structures. J. Cheminformatics 15(1): 77 (2023) - 2022
- [j20]Hyunho Kim, Minsu Park, Ingoo Lee, Hojung Nam:
BayeshERG: a robust, reliable and interpretable deep learning model for predicting hERG channel blockers. Briefings Bioinform. 23(4) (2022) - [j19]Ingoo Lee, Hojung Nam:
Sequence-based prediction of protein binding regions and drug-target interactions. J. Cheminformatics 14(1): 5 (2022) - [j18]Eunyoung Kim, Hojung Nam:
DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions. J. Cheminformatics 14(1): 9 (2022) - [i2]Ingoo Lee, Hojung Nam:
Infusing Linguistic Knowledge of SMILES into Chemical Language Models. CoRR abs/2205.00084 (2022) - 2021
- [j17]Iljung Jin, Hojung Nam:
HiDRA: Hierarchical Network for Drug Response Prediction with Attention. J. Chem. Inf. Model. 61(8): 3858-3867 (2021) - 2020
- [j16]Soobok Joe, Hojung Nam:
Prediction model construction of mouse stem cell pluripotency using CpG and non-CpG DNA methylation markers. BMC Bioinform. 21(1): 175 (2020) - [j15]Hyunho Kim, Hojung Nam:
hERG-Att: Self-attention-based deep neural network for predicting hERG blockers. Comput. Biol. Chem. 87: 107286 (2020) - [c9]Hyojung Paik, Sunyong Yoo, Hojung Nam, Mark Stevenson, Albert No:
DTMBIO 2020: The Fourteenth International Workshop on Data and Text Mining in Biomedical Informatics. CIKM 2020: 3537-3538
2010 – 2019
- 2019
- [j14]Eunyoung Kim, A.-Sol Choi, Hojung Nam:
Drug repositioning of herbal compounds via a machine-learning approach. BMC Bioinform. 20-S(10): 33-43 (2019) - [j13]Ingoo Lee, Jongsoo Keum, Hojung Nam:
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput. Biol. 15(6) (2019) - 2018
- [j12]Sunyong Yoo, Suhyun Ha, Moonshik Shin, Kyungrin Noh, Hojung Nam, Doheon Lee:
A Data-Driven Approach for Identifying Medicinal Combinations of Natural Products. IEEE Access 6: 58106-58118 (2018) - [j11]Ingoo Lee, Hojung Nam:
Identification of drug-target interaction by a random walk with restart method on an interactome network. BMC Bioinform. 19-S(8): 9-18 (2018) - [j10]Moonshik Shin, Donjin Jang, Hojung Nam, Kwang Hyung Lee, Doheon Lee:
Predicting the Absorption Potential of Chemical Compounds Through a Deep Learning Approach. IEEE ACM Trans. Comput. Biol. Bioinform. 15(2): 432-440 (2018) - [i1]Ingoo Lee, Jongsoo Keum, Hojung Nam:
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences. CoRR abs/1811.02114 (2018) - 2017
- [j9]Eunyoung Kim, Hojung Nam:
Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints. BMC Bioinform. 18(7): 25-34 (2017) - [c8]Sangwoo Kim, Hojung Nam:
In Silico Simulation of Signal Cascades in Biomedical Networks Based on the Production Rule System. ISBRA 2017: 356-361 - 2016
- [j8]Jongsoo Keum, Sunyong Yoo, Doheon Lee, Hojung Nam:
Prediction of compound-target interactions of natural products using large-scale drug and protein information. BMC Bioinform. 17(S-6): 219 (2016) - [j7]Soobok Joe, Hojung Nam:
Prognostic factor analysis for breast cancer using gene expression profiles. BMC Medical Informatics Decis. Mak. 16(S-1): 56 (2016) - 2015
- [j6]Junho Kim, Sanghyeon Kim, Hojung Nam, Sangwoo Kim, Doheon Lee:
SoloDel: a probabilistic model for detecting low-frequent somatic deletions from unmatched sequencing data. Bioinform. 31(19): 3105-3113 (2015) - [j5]Seunghwan Jung, Sejoon Lee, Sangwoo Kim, Hojung Nam:
Identification of genomic features in the classification of loss- and gain-of-function mutation. BMC Medical Informatics Decis. Mak. 15-S(S-1): S6 (2015) - [c7]Jongsoo Keum, Sunyong Yoo, Hojung Nam:
Prediction of Compound-Target Interactions of Natural Products Using Large-scale Drug and Protein Information. DTMBIO@CIKM 2015: 15 - [c6]Soobok Joe, Hojung Nam:
Prognostic Factor Analysis for Breast Cancer Using Gene Expression Profiles. DTMBIO@CIKM 2015: 16 - [c5]Eunyoung Kim, Sangwoo Kim, Suhyun Ha, Hojung Nam:
Toxicity Predictions using Compound Descriptions. DTMBIO@CIKM 2015: 21 - 2014
- [j4]Hojung Nam, Miguel Campodonico, Aarash Bordbar, Daniel R. Hyduke, Sangwoo Kim, Daniel C. Zielinski, Bernhard O. Palsson:
A Systems Approach to Predict Oncometabolites via Context-Specific Genome-Scale Metabolic Networks. PLoS Comput. Biol. 10(9) (2014) - [c4]Seunghwan Jung, Sejoon Lee, Sangwoo Kim, Hojung Nam:
Identification of Genomic Features in the Classification of Loss- and Gain-of-Function Mutation: [Extended Abstract]. DTMBIO@CIKM 2014: 23 - [c3]Soobok Joe, Hojung Nam:
Identification of a Specific Base Sequence of Pathogenic E. Coli through a Genomic Analysis. DTMBIO@CIKM 2014: 25-28 - [c2]Suhyun Ha, Sunyong Yoo, Moonshik Shin, Jin Sook Kwak, Oran Kwon, Min Chang Choi, Keon Wook Kang, Hojung Nam, Doheon Lee:
Integrative Database for Exploring Compound Combinations of Natural Products for Medical Effects. DTMBIO@CIKM 2014: 41 - 2011
- [j3]Sangwoo Kim, Hojung Nam, Doheon Lee:
Exploring molecular links between lymph node invasion and cancer prognosis in human breast cancer. BMC Syst. Biol. 5(S-2): S4 (2011)
2000 – 2009
- 2009
- [j2]Hojung Nam, Bong Chul Chung, Younghoon Kim, Ki Young Lee, Doheon Lee:
Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification. Bioinform. 25(23): 3151-3157 (2009) - [j1]Hojung Nam, Ki Young Lee, Doheon Lee:
Identification of temporal association rules from time-series microarray data sets. BMC Bioinform. 10(S-3) (2009) - 2008
- [c1]Hojung Nam, Ki Young Lee, Doheon Lee:
Identification of temporal association rules from time-series microarray data set: temporal association rules. DTMBIO 2008: 21-28
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