Ting-Yi Sung

1.7k total citations
32 papers, 1.0k citations indexed

About

Ting-Yi Sung is a scholar working on Molecular Biology, Oncology and Spectroscopy. According to data from OpenAlex, Ting-Yi Sung has authored 32 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Molecular Biology, 7 papers in Oncology and 7 papers in Spectroscopy. Recurrent topics in Ting-Yi Sung's work include Machine Learning in Bioinformatics (10 papers), Advanced Proteomics Techniques and Applications (7 papers) and Genomics and Phylogenetic Studies (7 papers). Ting-Yi Sung is often cited by papers focused on Machine Learning in Bioinformatics (10 papers), Advanced Proteomics Techniques and Applications (7 papers) and Genomics and Phylogenetic Studies (7 papers). Ting-Yi Sung collaborates with scholars based in Taiwan, United States and Spain. Ting-Yi Sung's co-authors include Wen-Lian Hsu, Emily Chia‐Yu Su, Yu‐Ju Chen, Jenn-Kang Hwang, Cheng‐Wei Cheng, Jing‐Ping Liou, Hsin-Nan Lin, Shinn‐Ying Ho, Hong-Jie Dai and Richard Tzong‐Han Tsai and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and PLoS ONE.

In The Last Decade

Ting-Yi Sung

32 papers receiving 988 citations

Peers

Ting-Yi Sung

SPECT

ONCOL

Ratna R. Thangudu United States

Yoshinobu Igarashi Japan

Patroklos Samaras Germany

Alain Gateau Switzerland

Mikhail A. Pyatnitskiy Russia

Amelie Stein Denmark

Hans‐Christian Ehrlich Germany

Stephan Aiche Germany

Wen-Lian Hsu Taiwan

Ratna R. Thangudu United States

Ting-Yi Sung

591 ×0.7

113 ×0.5

SPECT

90 ×0.8

ONCOL

42 ×0.5

137 ×1.8

Citations per year, relative to Ting-Yi Sung Ting-Yi Sung (= 1×) peers Ratna R. Thangudu

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

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20 of 20 papers shown

Sung, Ting-Yi, Han-Li Huang, Po‐Li Wei, et al.. (2021). EGFL6 promotes colorectal cancer cell growth and mobility and the anti‐cancer property of anti-EGFL6 antibody. Cell & Bioscience. 11(1). 53–53. 11 indexed citations

Kitata, Reta Birhanu, Wai-Kok Choong, Chia-Feng Tsai, et al.. (2021). A data-independent acquisition-based global phosphoproteomics system enables deep profiling. Nature Communications. 12(1). 2539–2539. 63 indexed citations

Huang, Han-Li, Yi-Min Liu, Ting-Yi Sung, et al.. (2019). TIMP3 expression associates with prognosis in colorectal cancer and its novel arylsulfonamide inducer, MPT0B390, inhibits tumor growth, metastasis and angiogenesis. Theranostics. 9(22). 6676–6689. 30 indexed citations

Sung, Ting-Yi, et al.. (2019). MPT0G413, A Novel HDAC6-Selective Inhibitor, and Bortezomib Synergistically Exert Anti-tumor Activity in Multiple Myeloma Cells. Frontiers in Oncology. 9. 249–249. 22 indexed citations

Tu, Huang‐Ju, Min‐Wu Chao, Ting-Yi Sung, et al.. (2018). The anticancer effects of MPT0G211, a novel HDAC6 inhibitor, combined with chemotherapeutic agents in human acute leukemia cells. Clinical Epigenetics. 10(1). 162–162. 25 indexed citations

Hsu, Kai‐Cheng, Hsueh‐Yun Lee, Tsui‐Chin Huang, et al.. (2018). A Novel Dual HDAC6 and Tubulin Inhibitor, MPT0B451, Displays Anti-tumor Ability in Human Cancer Cells in Vitro and in Vivo. Frontiers in Pharmacology. 9. 205–205. 26 indexed citations

Wang, Yi‐Ting, Szu‐Hua Pan, Chia-Feng Tsai, et al.. (2017). Phosphoproteomics Reveals HMGA1, a CK2 Substrate, as a Drug-Resistant Target in Non-Small Cell Lung Cancer. Scientific Reports. 7(1). 44021–44021. 28 indexed citations

Chang, Hui-Yin, et al.. (2016). iMet-Q: A User-Friendly Tool for Label-Free Metabolomics Quantitation Using Dynamic Peak-Width Determination. PLoS ONE. 11(1). e0146112–e0146112. 15 indexed citations

Lih, T. Mamie, Wai-Kok Choong, Cheng‐Wei Cheng, et al.. (2016). MAGIC-web: a platform for untargeted and targeted N-linked glycoprotein identification. Nucleic Acids Research. 44(W1). W575–W580. 9 indexed citations

10.

Cheng, Mei‐Ling, Yet‐Ran Chen, Chin Hsu, et al.. (2014). Metabolite Identification for Mass Spectrometry-Based Metabolomics Using Multiple Types of Correlated Ion Information. Analytical Chemistry. 87(4). 2143–2151. 55 indexed citations

11.

Chang, Jia‐Ming, Ionas Erb, Ting-Yi Sung, et al.. (2013). Efficient and Interpretable Prediction of Protein Functional Classes by Correspondence Analysis and Compact Set Relations. PLoS ONE. 8(10). e75542–e75542. 6 indexed citations

12.

Su, Emily Chia‐Yu, Jia‐Ming Chang, Cheng‐Wei Cheng, Ting-Yi Sung, & Wen-Lian Hsu. (2012). Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing. BMC Bioinformatics. 13(S17). S13–S13. 10 indexed citations

13.

Hsu, Chia‐Lang, Yi-Ting Wang, Yu‐Ju Chen, et al.. (2011). Phosphoproteomics Identifies Oncogenic Ras Signaling Targets and Their Involvement in Lung Adenocarcinomas. PLoS ONE. 6(5). e20199–e20199. 30 indexed citations

14.

Tsou, Chih‐Chiang, et al.. (2009). MaXIC-Q Web: a fully automated web service using statistical and computational methods for protein quantitation based on stable isotope labeling and LC–MS. Nucleic Acids Research. 37(suppl_2). W661–W669. 8 indexed citations

15.

Lin, Hsin-Nan, Ching-Tai Chen, Ting-Yi Sung, Shinn‐Ying Ho, & Wen-Lian Hsu. (2009). Protein subcellular localization prediction of eukaryotes using a knowledge-based approach. BMC Bioinformatics. 10(S15). S8–S8. 44 indexed citations

16.

Cheng, Cheng‐Wei, Emily Chia‐Yu Su, Jenn-Kang Hwang, Ting-Yi Sung, & Wen-Lian Hsu. (2008). Predicting RNA-binding sites of proteins using support vector machines and evolutionary information. BMC Bioinformatics. 9(S12). S6–S6. 108 indexed citations

17.

Hsu, Chun-Nan, Jin-Mei Lai, Chia-Hung Liu, et al.. (2007). Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (E ncyclopedia of H epatocellular C arcinoma genes O nline). BMC Bioinformatics. 8(1). 66–66. 42 indexed citations

18.

Su, Emily Chia‐Yu, et al.. (2007). Protein subcellular localization prediction based on compartment-specific features and structure conservation. BMC Bioinformatics. 8(1). 330–330. 55 indexed citations

19.

Chang, Jia‐Ming, Chi‐Fon Chang, Wen‐Jin Wu, et al.. (2006). RIBRA—An Error-Tolerant Algorithm for the NMR Backbone Assignment Problem. Journal of Computational Biology. 13(2). 229–244. 15 indexed citations

20.

He, Ding, et al.. (2005). Enhance Genomic IR with Term Variation and Expansion: Experiences of the IASL Group at Genomic Track 2005. 3 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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