Young‐Rae Cho

1.4k total citations
79 papers, 935 citations indexed

About

Young‐Rae Cho is a scholar working on Molecular Biology, Computational Theory and Mathematics and Electrical and Electronic Engineering. According to data from OpenAlex, Young‐Rae Cho has authored 79 papers receiving a total of 935 indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Molecular Biology, 23 papers in Computational Theory and Mathematics and 8 papers in Electrical and Electronic Engineering. Recurrent topics in Young‐Rae Cho's work include Bioinformatics and Genomic Networks (50 papers), Computational Drug Discovery Methods (22 papers) and Microbial Metabolic Engineering and Bioproduction (18 papers). Young‐Rae Cho is often cited by papers focused on Bioinformatics and Genomic Networks (50 papers), Computational Drug Discovery Methods (22 papers) and Microbial Metabolic Engineering and Bioproduction (18 papers). Young‐Rae Cho collaborates with scholars based in United States, South Korea and China. Young‐Rae Cho's co-authors include Aidong Zhang, Woochang Hwang, Murali Ramanathan, Aidong Zhang, Xiang Wu, Jong-Hoon Park, Yi Liu, Ying Liu, Lei Shi and Yanxin Lu and has published in prestigious journals such as Bioinformatics, PLoS ONE and Advanced Functional Materials.

In The Last Decade

Young‐Rae Cho

74 papers receiving 904 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Young‐Rae Cho United States 17 566 272 118 93 89 79 935
Rui Kuang United States 24 987 1.7× 148 0.5× 128 1.1× 46 0.5× 249 2.8× 74 1.6k
Jin Xu China 21 1.5k 2.6× 207 0.8× 269 2.3× 57 0.6× 168 1.9× 128 1.9k
Lee Sael South Korea 21 698 1.2× 301 1.1× 21 0.2× 113 1.2× 216 2.4× 60 1.3k
Aritra Chowdhury United States 14 514 0.9× 115 0.4× 79 0.7× 16 0.2× 199 2.2× 40 1.1k
Isaac T. Yonemoto United States 10 293 0.5× 129 0.5× 102 0.9× 10 0.1× 54 0.6× 14 588
Zhaohui Li China 12 107 0.2× 193 0.7× 237 2.0× 17 0.2× 87 1.0× 86 673
Pekka Orponen Finland 17 701 1.2× 243 0.9× 137 1.2× 34 0.4× 324 3.6× 58 1.4k
Concettina Guerra United States 13 258 0.5× 92 0.3× 19 0.2× 34 0.4× 64 0.7× 38 454

Countries citing papers authored by Young‐Rae Cho

Since Specialization
Citations

This map shows the geographic impact of Young‐Rae Cho'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 Young‐Rae Cho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Young‐Rae Cho more than expected).

Fields of papers citing papers by Young‐Rae Cho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Young‐Rae Cho. 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 Young‐Rae Cho. The network helps show where Young‐Rae Cho may publish in the future.

Co-authorship network of co-authors of Young‐Rae Cho

This figure shows the co-authorship network connecting the top 25 collaborators of Young‐Rae Cho. A scholar is included among the top collaborators of Young‐Rae Cho 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 Young‐Rae Cho. Young‐Rae Cho 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.
2.
Park, Jong-Hoon, et al.. (2025). MEMOL: Mixture of experts for multimodal learning through multi-head attention to predict drug toxicity. Computer Methods and Programs in Biomedicine. 273. 109088–109088.
3.
Park, Jong-Hoon & Young‐Rae Cho. (2025). DRAW+: network-based computational drug repositioning with attention walking and noise filtering. Health Information Science and Systems. 13(1). 14–14. 1 indexed citations
4.
Park, Jong-Hoon & Young‐Rae Cho. (2024). Computational drug repositioning with attention walking. Scientific Reports. 14(1). 10072–10072. 4 indexed citations
5.
Cho, Young‐Rae, et al.. (2024). GraphMHC: Neoantigen prediction model applying the graph neural network to molecular structure. PLoS ONE. 19(3). e0291223–e0291223. 4 indexed citations
6.
Cho, Young‐Rae, et al.. (2023). Predicting Drug–Gene–Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning. Biomedicines. 11(7). 1998–1998. 9 indexed citations
7.
Liu, Yi, Ying Liu, Xiang Wu, & Young‐Rae Cho. (2022). High performance aqueous zinc battery enabled by potassium ion stabilization. Journal of Colloid and Interface Science. 628(Pt B). 33–40. 48 indexed citations
8.
Cho, Young‐Rae, et al.. (2021). Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions. Methods. 198. 19–31. 27 indexed citations
9.
Kim, Minkyun, et al.. (2020). Effects of Adherend Thickness on Adhesive Strength between Organic Adhesive and Metal Adherend. 27(4). 127–133. 1 indexed citations
10.
11.
Cho, Young‐Rae, et al.. (2019). LePrimAlign: local entropy-based alignment of PPI networks to predict conserved modules. BMC Genomics. 20(S9). 964–964. 4 indexed citations
12.
Aryal, Um Kanta, Kumarasamy Gunasekar, Ho‐Yeol Park, et al.. (2017). Triazine-based Polyelectrolyte as an Efficient Cathode Interfacial Material for Polymer Solar Cells. ACS Applied Materials & Interfaces. 9(29). 24753–24762. 20 indexed citations
13.
Cho, Young‐Rae, et al.. (2012). Accuracy improvement in protein complex prediction from protein interaction networks by refining cluster overlaps. Proteome Science. 10(Suppl 1). S3–S3. 6 indexed citations
14.
Cho, Young‐Rae & Aidong Zhang. (2010). Identification of functional hubs and modules by converting interactome networks into hierarchical ordering of proteins. BMC Bioinformatics. 11(S3). S3–S3. 15 indexed citations
15.
Bae, D.H., et al.. (2009). Effect of Pre-Heat Treatment on Bonding Properties in Ti/Al/STS Clad Materials. Korean Journal of Metals and Materials. 47(9). 573–579. 11 indexed citations
16.
Cho, Young‐Rae, Lei Shi, Murali Ramanathan, & Aidong Zhang. (2008). A probabilistic framework to predict protein function from interaction data integrated with semantic knowledge. BMC Bioinformatics. 9(1). 382–382. 14 indexed citations
17.
Hwang, Woochang, Young‐Rae Cho, Aidong Zhang, & Murali Ramanathan. (2008). CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions. BMC Bioinformatics. 9(1). 64–64. 18 indexed citations
18.
Cho, Young‐Rae, Woochang Hwang, Murali Ramanathan, & Aidong Zhang. (2007). Semantic integration to identify overlapping functional modules in protein interaction networks. BMC Bioinformatics. 8(1). 265–265. 114 indexed citations
19.
Hwang, Woochang, Young‐Rae Cho, Aidong Zhang, & Murali Ramanathan. (2006). A novel functional module detection algorithm for protein-protein interaction networks. Algorithms for Molecular Biology. 1(1). 24–24. 64 indexed citations
20.
Cho, Young‐Rae, et al.. (2005). The study of binocular function of college students. Journal of Korean Ophthalmic Optics Society. 10(2). 103–110. 1 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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