Ting‐Yi Sung

1.7k total citations
74 papers, 1.3k citations indexed

About

Ting‐Yi Sung is a scholar working on Molecular Biology, Computer Networks and Communications and Spectroscopy. According to data from OpenAlex, Ting‐Yi Sung has authored 74 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Molecular Biology, 23 papers in Computer Networks and Communications and 19 papers in Spectroscopy. Recurrent topics in Ting‐Yi Sung's work include Interconnection Networks and Systems (21 papers), Advanced Proteomics Techniques and Applications (18 papers) and Machine Learning in Bioinformatics (17 papers). Ting‐Yi Sung is often cited by papers focused on Interconnection Networks and Systems (21 papers), Advanced Proteomics Techniques and Applications (18 papers) and Machine Learning in Bioinformatics (17 papers). Ting‐Yi Sung collaborates with scholars based in Taiwan, United States and Australia. Ting‐Yi Sung's co-authors include Wen−Lian Hsu, Wen‐Lian Hsu, Lih-Hsing Hsu, Yu‐Ju Chen, Chien-Ping Chang, Wai-Kok Choong, Manfred Padberg, Chia‐Feng Tsai, Richard Tzong‐Han Tsai and Lih‐Hsing Hsu and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and PLoS ONE.

In The Last Decade

Ting‐Yi Sung

69 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ting‐Yi Sung Taiwan 20 748 289 238 223 129 74 1.3k
Weixun Wang United States 19 925 1.2× 452 1.6× 204 0.9× 187 0.8× 60 0.5× 48 1.8k
Kun‐Mao Chao Taiwan 23 741 1.0× 49 0.2× 182 0.8× 444 2.0× 303 2.3× 85 1.6k
Ming‐Yang Kao United States 23 411 0.5× 152 0.5× 774 3.3× 502 2.3× 418 3.2× 92 1.6k
Dekel Tsur Israel 12 490 0.7× 273 0.9× 93 0.4× 192 0.9× 150 1.2× 49 831
Lenore Cowen United States 24 1.3k 1.7× 39 0.1× 339 1.4× 178 0.8× 811 6.3× 91 2.4k
Lee Sael South Korea 21 698 0.9× 62 0.2× 56 0.2× 216 1.0× 301 2.3× 60 1.3k
Frank Dehne Canada 22 596 0.8× 22 0.1× 396 1.7× 209 0.9× 338 2.6× 119 1.6k
Xiaoying Jia United States 26 1.3k 1.7× 109 0.4× 399 1.7× 284 1.3× 38 0.3× 83 2.8k
María García de la Banda Australia 19 335 0.4× 15 0.1× 285 1.2× 463 2.1× 252 2.0× 81 1.1k
Jin Xu China 21 1.5k 2.0× 67 0.2× 45 0.2× 168 0.8× 207 1.6× 128 1.9k

Countries citing papers authored by Ting‐Yi Sung

Since Specialization
Citations

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

Fields of papers citing papers by Ting‐Yi Sung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ting‐Yi Sung. 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 Ting‐Yi Sung. The network helps show where Ting‐Yi Sung may publish in the future.

Co-authorship network of co-authors of Ting‐Yi Sung

This figure shows the co-authorship network connecting the top 25 collaborators of Ting‐Yi Sung. A scholar is included among the top collaborators of Ting‐Yi Sung 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 Ting‐Yi Sung. Ting‐Yi Sung 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.
Sung, Ting‐Yi, Shiow‐Lin Pan, Wei‐Jan Huang, et al.. (2023). The study of a novel CDK8 inhibitor E966-0530–45418 that inhibits prostate cancer metastasis in vitro and in vivo. Biomedicine & Pharmacotherapy. 162. 114667–114667. 14 indexed citations
3.
Wang, Jenhung, et al.. (2022). Calibr improves spectral library search for spectrum-centric analysis of data independent acquisition proteomics. Scientific Reports. 12(1). 2045–2045. 11 indexed citations
4.
Wang, Jenhung, et al.. (2021). Multi-Q 2 software facilitates isobaric labeling quantitation analysis with improved accuracy and coverage. Scientific Reports. 11(1). 2233–2233. 3 indexed citations
5.
Choong, Wai-Kok, et al.. (2020). MinProtMaxVP: Generating a minimized number of protein variant sequences containing all possible variant peptides for proteogenomic analysis. Journal of Proteomics. 223. 103819–103819. 4 indexed citations
6.
Lin, Hsin-Nan, et al.. (2019). N-GlyDE: a two-stage N-linked glycosylation site prediction incorporating gapped dipeptides and pattern-based encoding. Scientific Reports. 9(1). 15975–15975. 49 indexed citations
7.
Sung, Ting‐Yi, et al.. (2013). Changing the Diameter in a Diagonal Mesh Network. Journal of information science and engineering. 29. 193–208. 1 indexed citations
8.
Lai, Jhih‐Siang, et al.. (2013). Lipid exposure prediction enhances the inference of rotational angles of transmembrane helices. BMC Bioinformatics. 14(1). 304–304. 10 indexed citations
9.
Sudhir, Putty‐Reddy, Chein‐Hung Chen, Mei-Jung Wang, et al.. (2012). Label-free Quantitative Proteomics and N-Glycoproteomics Analysis of KRAS-activated Human Bronchial Epithelial Cells. Molecular & Cellular Proteomics. 11(10). 901–915. 22 indexed citations
10.
Lai, Jhih‐Siang, Cheng‐Wei Cheng, Ting‐Yi Sung, & Wen‐Lian Hsu. (2012). Computational Comparative Study of Tuberculosis Proteomes Using a Model Learned from Signal Peptide Structures. PLoS ONE. 7(4). e35018–e35018. 8 indexed citations
11.
Cheng, Cheng‐Wei, et al.. (2010). TMPad: an integrated structural database for helix-packing folds in transmembrane proteins. Nucleic Acids Research. 39(suppl_1). D347–D355. 19 indexed citations
12.
Chang, Jia‐Ming, et al.. (2008). PSLDoc: Protein subcellular localization prediction based on gapped‐dipeptides and probabilistic latent semantic analysis. Proteins Structure Function and Bioinformatics. 72(2). 693–710. 38 indexed citations
13.
Tsai, Richard Tzong‐Han, Shih-Hung Wu, Wen‐Chi Chou, et al.. (2006). Various criteria in the evaluation of biomedical named entity recognition. BMC Bioinformatics. 7(1). 92–92. 85 indexed citations
14.
Lin, Hsin-Nan, et al.. (2005). HYPROSP II-A knowledge-based hybrid method for protein secondary structure prediction based on local prediction confidence. Bioinformatics. 21(15). 3227–3233. 38 indexed citations
15.
Tsai, Richard Tzong‐Han, et al.. (2004). A maximum entropy approach to biomedical named entity recognition. 27(2). 56–61. 41 indexed citations
16.
Sung, Ting‐Yi, et al.. (2001). A Family of Trivalent 1-Hamiltonian Graphs With Diameter O(log n). Journal of information science and engineering. 17(4). 535–548. 1 indexed citations
17.
Sung, Ting‐Yi, et al.. (2000). Optimal k-fault-tolerant networks for token rings. Journal of information science and engineering. 16(3). 381–390. 3 indexed citations
18.
Hsu, Lih-Hsing, et al.. (1997). Extended Tree Contraction for Some parallel Algorithms on Series-Parallel Networks.. Journal of information science and engineering. 13. 665–680. 1 indexed citations
19.
Sung, Ting‐Yi, et al.. (1992). DATA STRUCTURE FOR GRAPH REPRESENTATIONS OF A NETWORK HAVING DOUBLE EULER TRAILS. 12. 436–442. 1 indexed citations
20.
Sung, Ting‐Yi. (1989). Contributions to the travelling salesman problem and its variants. UMI eBooks. 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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