Hojung Nam

4.3k total citations · 3 hit papers
66 papers, 2.9k citations indexed

About

Hojung Nam is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Hojung Nam has authored 66 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 22 papers in Computational Theory and Mathematics and 9 papers in Materials Chemistry. Recurrent topics in Hojung Nam's work include Computational Drug Discovery Methods (22 papers), Bioinformatics and Genomic Networks (16 papers) and Protein Structure and Dynamics (8 papers). Hojung Nam is often cited by papers focused on Computational Drug Discovery Methods (22 papers), Bioinformatics and Genomic Networks (16 papers) and Protein Structure and Dynamics (8 papers). Hojung Nam collaborates with scholars based in South Korea, United States and Austria. Hojung Nam's co-authors include Ingoo Lee, Bernhard Ø. Palsson, Joshua A. Lerman, Tom M Conrad, Adam M. Feist, Jeffrey D. Orth, Doheon Lee, Eunyoung Kim, Nathan E. Lewis and Dae‐Hee Lee and has published in prestigious journals such as Nature, Science and Nucleic Acids Research.

In The Last Decade

Hojung Nam

59 papers receiving 2.9k citations

Hit Papers

A comprehensive genome‐scale reconstruction of Escherichi... 2011 2026 2016 2021 2011 2019 2019 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A comprehensive genome‐scale reconstruction of Escherichia coli metabolism—2011
    2011 767 citations Jeffrey D. Orth, Tom M Conrad et al. Molecular Systems Biology profile →
  2. DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
    2019 414 citations Ingoo Lee, Hojung Nam et al. PLoS Computational Biology profile →
  3. The CH25H–CYP7B1–RORα axis of cholesterol metabolism regulates osteoarthritis
    2019 235 citations Wan-Su Choi, Gyuseok Lee et al. Nature profile →

Peers

Hojung Nam
Shanrong Zhao United States
Brian Kelley United States
Michaela Spitzer United Kingdom
Rengül Çetin-Atalay Türkiye
George M. Spyrou Greece
William P. Janzen United States
Ying Zhou China
Dokyun Na South Korea
Leming Shi China
Petr Beneš Czechia
Shanrong Zhao United States
Hojung Nam
Citations per year, relative to Hojung Nam Hojung Nam (= 1×) peers Shanrong Zhao

Countries citing papers authored by Hojung Nam

Since Specialization
Citations

This map shows the geographic impact of Hojung Nam's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hojung Nam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hojung Nam more than expected).

Fields of papers citing papers by Hojung Nam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hojung Nam. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hojung Nam. The network helps show where Hojung Nam may publish in the future.

Co-authorship network of co-authors of Hojung Nam

This figure shows the co-authorship network connecting the top 25 collaborators of Hojung Nam. A scholar is included among the top collaborators of Hojung Nam based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hojung Nam. Hojung Nam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Nam, Hojung, et al.. (2025). Performance Enhancement of UWB Antennas Using Two Metal Frames in Mobile Devices. IEEE Antennas and Wireless Propagation Letters. 25(2). 606–610.
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Kim, Eunyoung & Hojung Nam. (2022). DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions. Journal of Cheminformatics. 14(1). 9–9. 24 indexed citations
5.
Kim, Tae‐Min, In Seok Yang, Sejoon Lee, et al.. (2020). Cross-species oncogenic signatures of breast cancer in canine mammary tumors. Nature Communications. 11(1). 3616–3616. 72 indexed citations
6.
Nam, Hojung, et al.. (2020). hERG-Att: Self-attention-based deep neural network for predicting hERG blockers. Computational Biology and Chemistry. 87. 107286–107286. 33 indexed citations
7.
Lee, Ingoo, et al.. (2019). DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Computational Biology. 15(6). e1007129–e1007129. 414 indexed citations breakdown →
8.
Choi, Wan-Su, Gyuseok Lee, Won‐Hyun Song, et al.. (2019). The CH25H–CYP7B1–RORα axis of cholesterol metabolism regulates osteoarthritis. Nature. 566(7743). 254–258. 235 indexed citations breakdown →
9.
Yoo, Sunyong, Hojung Nam, & Doheon Lee. (2018). Phenotype-oriented network analysis for discovering pharmacological effects of natural compounds. Scientific Reports. 8(1). 11667–11667. 12 indexed citations
10.
Yoo, Sunyong, et al.. (2018). In silico profiling of systemic effects of drugs to predict unexpected interactions. Scientific Reports. 8(1). 1612–1612. 14 indexed citations
11.
Shin, Moonshik, et al.. (2016). Predicting the Absorption Potential of Chemical Compounds Through a Deep Learning Approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 15(2). 432–440. 31 indexed citations
12.
Yoo, Sunyong, et al.. (2016). Prediction of compound-target interactions of natural products using large-scale drug and protein information. BMC Bioinformatics. 17(S6). 219–219. 20 indexed citations
13.
Nam, Hojung, et al.. (2016). Prognostic factor analysis for breast cancer using gene expression profiles. BMC Medical Informatics and Decision Making. 16(S1). 56–56. 10 indexed citations
14.
Nam, Hojung, Miguel A. Campodonico, Aarash Bordbar, et al.. (2014). A Systems Approach to Predict Oncometabolites via Context-Specific Genome-Scale Metabolic Networks. PLoS Computational Biology. 10(9). e1003837–e1003837. 55 indexed citations
15.
Kim, Sangwoo, Hojung Nam, & Doheon Lee. (2011). Exploring molecular links between lymph node invasion and cancer prognosis in human breast cancer. BMC Systems Biology. 5(S2). S4–S4. 6 indexed citations
16.
Orth, Jeffrey D., Tom M Conrad, Joshua A. Lerman, et al.. (2011). A comprehensive genome‐scale reconstruction of Escherichia coli metabolism—2011. Molecular Systems Biology. 7(1). 535–535. 767 indexed citations breakdown →
17.
Nam, Hojung, Ki‐Young Lee, & Doheon Lee. (2009). Identification of temporal association rules from time-series microarray data sets. BMC Bioinformatics. 10(S3). S6–S6. 23 indexed citations
18.
Nam, Hojung, Jinwon Lee, & Doheon Lee. (2009). Computational identification of altered metabolism using gene expression and metabolic pathways. Biotechnology and Bioengineering. 103(4). 835–843. 10 indexed citations
19.
Ryu, Taewoo, Sunjae Lee, Hojung Nam, et al.. (2007). bZIPDB : A database of regulatory information for human bZIP transcription factors. BMC Genomics. 8(1). 136–136. 14 indexed citations
20.
Oh, Yoo Jin, et al.. (2005). Nanoscale observation of domain configuration and switching of ferroelectric nanolayers. Journal of the Korean Physical Society. 46(1). 341–345. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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