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҂֛ᛄ
Sung, Ting-Yi ਿ ͉ ༟ ࣘ Research Description
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Research Description
ᔖcc၈jӺࡰResearch Fellow (2000--) My current research interest is in bioinfor- 2005 TREC Genomics Track Ad-hoc Retrieval Con-
th
matics with focus on protein structure prediction, test and were ranked the 6 among 32 teams. In the
௰৷ኪዝj Ph.D., Operations Research, biomedical literature mining, and quantitative pro- area of biomedical information extraction, we focus
New York University (1989) teomics based on high-throughput mass spectrom- on named entity relation recognition. We have de-
etry data. veloped a semi-automatically approach to annotate
ཥcc༑j+886-2-2788-3799 ext. 1711 our biomedical proposition bank, called BioProp,
Protein structure prediction which will be used to train an automatic semantic
ෂccॆj+886-2-2782-4814
We have developed a knowledge-based ap- role labeling system in biomedical domain.
ཥɿڦᇌjtsung@iis.sinica.edu.tw proach for protein secondary structure prediction Research Fellows
which is the core of our proposed hybrid prediction Quantitative proteomics based on high-through-
ၣccࠫjhttp://www.iis.sinica.edu.tw/pages/tsung method. Using similar approach, we develop a local put mass spectrometry data
structure prediction method and use it for subse- Various stable isotope labeling techniques,
quent tertiary structure prediction. Moreover, we e.g., ICAT and iTRAQ, followed by liquid chroma-
use machine learning approaches to predict specific tography-tandem mass spectrometry (LC-MS/MS)
Ӻᔊʧ
Ӻᔊʧ
structures, e.g., transmembrane helices and their to- is frequently used to quantify protein expression.
• Associate Research Fellow, Institute of Information pology, and beta turns. We have developed a fully automated tool for mul-
Research Fellows
S ҢࡁٙӺჯਹމ͛ي༟ৃdӺ˴ᕚܼ̍j tiplexed quantitation using iTRAQ labeling, called
Science, Academia Sinica (1989-2000)
Biomedical literature mining Multi-Q. In addition, we are developing a quantita-
• M.Ph., Operations Research, New York University ஐͣሯഐཫe͛ي˖ᘠઞਖeԴ͜ሯᗅᄃ༟ࣘ
We apply natural language processing tech- tion tool for ICAT labeling. More quantitation and
ٙஐͣሯ֛ඎʱؓʘӻ୕ක೯ഃf niques to information retrieval and information ex-
• MBA, State University of New York at Buffalo visualization tools for large-scale proteomics will be
traction in biomedical domain. We participated the developed.
(1983)
ίஐͣሯഐཫ˙ࠦdҢࡁ̈˸ٝᗆࢫ
• B.S., Management Science, National Chiao Tung
މਿᓾٙɚॴഐཫ˙جdҢࡁɰ০࿁membrane
University (1980) Selected Publications
Selected Publications
proteinsආБɚॴഐ̍ўtopologyཫd˸
• Honor: The Ten Outstanding Young Women Award,
ʿत֛ٙɚॴഐආБཫfҢࡁͦۃ˸local 1. Chien-Ping Chang, Ting-Yi Sung and Lih-Hsing Hsu, Edge congestion Computer Society Computational Systems Bioinformatics Conference
1998 ୋɤɖ֣ɤɽ௫̈ɾڡϋ and topological properties of crossed cubes, IEEE Transactions on (CSB), 2003.
structure predictionމਿᓾd೯࢝ɧॴഐཫf
Parallel and Distributed Systems 11, 2000, 64--80. 11. Yi-Feng Lin, Tzong-Han Tsai, Wen-Chi Chou, Kuen-Pin Wu, Ting-Yi
2. Jeng-Jung Wang, Chun-Nan Hung, Jimmy J.M. Tan, Lih-Hsing Hsu Sung and Wen-Lian Hsu, A maximum entropy approach to biomedical
ί͛ي˖ᘠઞਖ˙ࠦdҢࡁл͜І್ႧԊஈଣ and Ting-Yi Sung, Construction schemes for fault tolerant Hamilto- named entity recognition, Proceedings of the 4th ACM SIGKDD Work-
nian graphs, Networks 35, 2000, 233-245. shop on Data Mining in Bioinformatics (BioKDD 2004), pages 56-61,
ٙҦஔආБ͛ي༟ৃᏨ॰ၾ༟ৃᓘ՟ٙӺ˴ᕚd 3. Chun-Nan Hung, Jeng-Jung Wang, Ting-Yi Sung and Lih-Hsing Hsu, 2004.
On the isomorphism between cyclic-cubes and wrapped butterfly 12. Kuen-Pin Wu, Hsin-Nan Lin, Jia-Ming Chang, Ting-Yi Sung and Wen-
п͛يኪԘήҬՑᗫٙ˖ᘠ༟ࣘfҢࡁ networks, IEEE Transactions of Parallel and Distributed Systems 11, Lian Hsu, HYPROSP: a hybrid protein secondary structure prediction
2005ϋਞ̋TREC Genomics Trackٙad hoc taskٙ͛ 864, 2000. algorithm—a knowledge-based approach, Nucleic Acids Research,
4. Jeng-Jung Wang, Ting-Yi Sung and Lih-Hsing Hsu, A family of triva- volume 32, number 17, pages 5059-5065, 2004.
ي༟ৃᏨ॰ᘩᒄdί32ඟٙਞᒄ٫ʕᐏୋʬΤf lent 1-Hamiltonian graphs with diameter O(log n), Journal of Infor- 13. Kuen-Pin Wu, Jia-Ming Chang, Jun-Bo Chen, Chi-Fon Chang,
mation Science and Engineering 17, 2001, 435—448; a preliminary Wen-Jin Wu, Tai-Huang Huang, Ting-Yi Sung and Wen-Lian Hsu,
͛ي༟ৃᓘ՟ٙӺ˴ᕚdܼ̍˖ᘠʕ͛يΤ൚ٙ version under the title "A new family of optimal 1-hamiltonian graphs RIBRA-an error-tolerant algorithm for the NMR backbone assign-
with small diameter" also appeared in Computing and Combinatorics, ment problem, to appear in Journal of Computational Biology; also
፫ᗆd˸ʿ͛يΤ൚ගʝᗫڷʘ፫ᗆiԷνjஐ Proceedings of the Fourth Annual International Conference, CO- in Proceedings of the International Conference on Research in Com-
COON'97, Lecture Notes in Computer Science 1449, Wen-Lian Hsu putational Molecular Biology (RECOMB’05), acceptance rate: 18%
ͣሯගʹʝЪ͜eਿΪၾशषഃʘᗫڷfމəආБ and Ming-Yang Kao, editors, Springer-Verlag, 269--278, 1998. (39/217).
Τ൚ගᗫڷ፫ᗆٙӺdҢࡁԨ೯࢝͛يᗫڷٙႧ 5. Ting-Yi Sung, Tung-Yang Ho, Chien-Ping Chang and Lih-Hsing Hsu, 14. Hsin-Nan Lin, Jia-Ming Chang, Kuen-Pin Wu, Ting-Yi Sung and Wen-
Optimal k-fault-tolerant networks for token rings, Journal of Informa- Lian Hsu, A knowledge-based hybrid method for protein secondary
ࣘࢫf tion Science and Engineering 16, 2000, 381--390. structure prediction based on local prediction confi dence, Bioinformat-
6. Chun-Nan Hung, Lih-Hsing Hsu and Ting-Yi Sung, On the construc- ics, volume 21, pages 3227-3233, 2005.
tion of combined k-fault-tolerant hamiltonian graphs, Networks 37, 15. Hsin-Nan Lin, Kuen-Pin Wu, Jia-Ming Chang, Ting-Yi Sung and Wen-
ίᗅᄃ༟ࣘٙஐͣሯ֛ඎʱؓ˙ࠦdҢࡁߧ 2001, 165—170; a preliminary version also in the Proceedings of 1998 Lian Hsu, GANA – a genetic algorithm for NMR backbone resonance
International Computer Symposium Workshop on Algorithms, 1--5. assignment, Nucleic Acids Research, volume 33, 4593-4601, 2005
ɢ೯࢝ІਗʷʱؓʈՈfҢࡁʊҁϓɓࢁΤމ 7. Tseng-Kuei Li, Jimmy J.M. Tan, Ting-Yi Sung and Lih-Hsing Hsu, (Impact Factor: 7.26).
Optimum congested routing strategy on twisted cubes, Journal of In- 16. Tung-Yang Ho, Ting-Yi Sung and Lih-Hsing Hsu, A note on edge fault
Multi-Qٙழӻ୕dஈଣ˸iTRAQމᅺൗ֛ٙඎ˙
terconnection Networks 1, 2000, 115--134. tolerance with respect to hypercubes, Applied Mathematics Letters,
جʘ༟ࣘʱؓiϤ̮dҢࡁ͍ί೯࢝ɓࢁԴ͜ICAT 8. Chun-Nan Hung, Lih-Hsing Hsu and Ting-Yi Sung, On the construc- volume 18, pages 1125--1128, 2005.
tion of combined k-fault-tolerant Hamiltonian graphs, Networks 37, 17. Tzong-Han Tsai, Shih-Hung Wu, Wen-Chi Chou, Yu-Chun Lin, Ding
֛ඎҦஔٙʱؓӻ୕f͊ԸҢࡁਗ਼೯࢝ʔΝ֛ٙඎ 2001, 165—170; a preliminary version also in the Proceedings of 1998 He, Ting-Yi Sung and Wen-Lian Hsu, Various criteria in the evalua-
International Computer Symposium Workshop on Algorithms, 1--5. tion of biomedical named entity recognition, to appear in BMC Bioin-
ழdᏐ͜ɽඎٙஐͣሯኪʘӺiԨ೯࢝ൖ 9. Tseng-Kuei Li, Jimmy J.M. Tan, Lih-Hsing Hsu and Ting-Yi Sung, formatics (Impact Factor: 5.42).
The shuffle-cubes and their generalization, Information Processing 18. Ching-Tai Chen, Hsin-Nan Lin, Kun-Pin Wu, Ting-Ying Sung and
ᙂʷʈՈdᜫԴ͜٫һ˙ک༆ᛘՉྼ᜕ᅰኽאഐ Letters 77, 2001, 35--41. Wen-Lian Hsu, A Knowledge-based Approach to Protein Local Struc-
؈f 10. Kuen-Pin Wu, Hsin-Nan Lin, Ting-Yi Sung and Wen-Lian Hsu, A new ture Prediction, Proceedings of Asia Pacific Bioinformatics Confer-
nd
similarity measure among protein sequences, 2 International IEEE ence (APBC), 2006.
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