Sangsoo Lim

758 total citations
26 papers, 481 citations indexed

About

Sangsoo Lim is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cancer Research. According to data from OpenAlex, Sangsoo Lim has authored 26 papers receiving a total of 481 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 8 papers in Computational Theory and Mathematics and 6 papers in Cancer Research. Recurrent topics in Sangsoo Lim's work include Bioinformatics and Genomic Networks (11 papers), Gene expression and cancer classification (9 papers) and Computational Drug Discovery Methods (8 papers). Sangsoo Lim is often cited by papers focused on Bioinformatics and Genomic Networks (11 papers), Gene expression and cancer classification (9 papers) and Computational Drug Discovery Methods (8 papers). Sangsoo Lim collaborates with scholars based in South Korea, United States and Malaysia. Sangsoo Lim's co-authors include Sun Kim, Sangseon Lee, Youngjun Park, Jung-Woo Kim, Jin‐Wu Nam, Youngkuk Kim, Hahn Nahmgoong, Jae Bum Kim, Inuk Jung and Taeheon Lee and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Bioinformatics.

In The Last Decade

Sangsoo Lim

24 papers receiving 474 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sangsoo Lim South Korea 13 334 151 69 64 38 26 481
Yuansheng Liu China 11 425 1.3× 104 0.7× 64 0.9× 21 0.3× 65 1.7× 23 584
Mohan Rao United States 7 215 0.6× 153 1.0× 38 0.6× 19 0.3× 19 0.5× 13 446
Reiji Teramoto Japan 10 327 1.0× 128 0.8× 39 0.6× 37 0.6× 19 0.5× 21 470
Sangseon Lee South Korea 12 235 0.7× 93 0.6× 54 0.8× 20 0.3× 60 1.6× 40 363
Ziqi Pan China 13 263 0.8× 111 0.7× 48 0.7× 15 0.2× 47 1.2× 28 502
Yaping Fang China 16 573 1.7× 57 0.4× 91 1.3× 32 0.5× 25 0.7× 37 792
Jonathan Wagg Switzerland 8 480 1.4× 65 0.4× 44 0.6× 26 0.4× 27 0.7× 20 645
Xingjian Chen China 12 264 0.8× 105 0.7× 46 0.7× 12 0.2× 41 1.1× 41 452
Hao Dai China 14 443 1.3× 117 0.8× 56 0.8× 14 0.2× 53 1.4× 40 654
Ted M. Lakowski Canada 17 649 1.9× 68 0.5× 37 0.5× 31 0.5× 20 0.5× 45 864

Countries citing papers authored by Sangsoo Lim

Since Specialization
Citations

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

Fields of papers citing papers by Sangsoo Lim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sangsoo Lim. 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 Sangsoo Lim. The network helps show where Sangsoo Lim may publish in the future.

Co-authorship network of co-authors of Sangsoo Lim

This figure shows the co-authorship network connecting the top 25 collaborators of Sangsoo Lim. A scholar is included among the top collaborators of Sangsoo Lim 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 Sangsoo Lim. Sangsoo Lim 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.
Lee, Sangseon, et al.. (2023). Exploring chemical space for lead identification by propagating on chemical similarity network. Computational and Structural Biotechnology Journal. 21. 4187–4195. 4 indexed citations
2.
Lim, Sangsoo, et al.. (2023). Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers. Nature Communications. 14(1). 3570–3570. 59 indexed citations
3.
Lim, Sangsoo, et al.. (2022). Supervised chemical graph mining improves drug-induced liver injury prediction. iScience. 26(1). 105677–105677. 17 indexed citations
4.
Lee, Gung, Ye Young Kim, Hagoon Jang, et al.. (2022). SREBP1c-PARP1 axis tunes anti-senescence activity of adipocytes and ameliorates metabolic imbalance in obesity. Cell Metabolism. 34(5). 702–718.e5. 64 indexed citations
5.
Lim, Sangsoo, et al.. (2022). On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach. Computational and Structural Biotechnology Journal. 20. 4288–4304. 13 indexed citations
6.
Lee, Sangseon, et al.. (2021). Subnetwork representation learning for discovering network biomarkers in predicting lymph node metastasis in early oral cancer. Scientific Reports. 11(1). 23992–23992. 2 indexed citations
7.
Jung, Inuk, et al.. (2021). MONTI: A Multi-Omics Non-negative Tensor Decomposition Framework for Gene-Level Integrative Analysis. Frontiers in Genetics. 12. 682841–682841. 12 indexed citations
8.
Lim, Sangsoo, et al.. (2021). A review on compound-protein interaction prediction methods: Data, format, representation and model. Computational and Structural Biotechnology Journal. 19. 1541–1556. 80 indexed citations
9.
Jeong, Dabin, et al.. (2021). Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis. Frontiers in Genetics. 12. 652623–652623. 1 indexed citations
10.
Park, Yoon Jeong, Sangseon Lee, Sangsoo Lim, et al.. (2021). DNMT1 maintains metabolic fitness of adipocytes through acting as an epigenetic safeguard of mitochondrial dynamics. Proceedings of the National Academy of Sciences. 118(11). 32 indexed citations
11.
Lee, Sangseon, Sangsoo Lim, Dabin Jeong, et al.. (2020). DRIM: A Web-Based System for Investigating Drug Response at the Molecular Level by Condition-Specific Multi-Omics Data Integration. Frontiers in Genetics. 11. 564792–564792. 13 indexed citations
12.
Lee, Sangseon, et al.. (2020). Cancer subtype classification and modeling by pathway attention and propagation. Bioinformatics. 36(12). 3818–3824. 33 indexed citations
13.
Lee, Sangseon, et al.. (2019). SpliceHetero: An information theoretic approach for measuring spliceomic intratumor heterogeneity from bulk tumor RNA-seq. PLoS ONE. 14(10). e0223520–e0223520. 7 indexed citations
14.
Lim, Sangsoo, et al.. (2019). Enhancer prediction with histone modification marks using a hybrid neural network model. Methods. 166. 48–56. 10 indexed citations
15.
Rhee, Sung-Min, et al.. (2017). Iterative segmented least square method for functional microRNA-mRNA module discovery in breast cancer. International Journal of Data Mining and Bioinformatics. 17(1). 25–25.
16.
17.
Park, Youngjun, Sangsoo Lim, Jin‐Wu Nam, & Sun Kim. (2016). Measuring intratumor heterogeneity by network entropy using RNA-seq data. Scientific Reports. 6(1). 37767–37767. 39 indexed citations
18.
Hur, Benjamin, Sangsoo Lim, Heejoon Chae, et al.. (2016). CLIP-GENE: a web service of the condition specific context-laid integrative analysis for gene prioritization in mouse TF knockout experiments. Biology Direct. 11(1). 57–57. 1 indexed citations
19.
Rhee, Sung-Min, Sangsoo Lim, & Sun Kim. (2016). Iterative segmented least square method for functional microRNA-mRNA module discovery in breast cancer. 490. 767–770. 1 indexed citations
20.

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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