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҂֛ᛄ

Sung, Ting-Yi ਿ ͉ ༟ ࣘ Research Description
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Research Description
ᔖcc၈j޼ӺࡰResearch 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 Ңࡁٙ޼Ӻჯਹމ͛ي༟ৃd޼Ӻ˴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 ஐͣሯഐ࿴ཫ಻e͛ي˖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)
ίஐͣሯഐ࿴ཫ಻˙ࠦdҢࡁ౤̈˸ٝᗆࢫ
• B.S., Management Science, National Chiao Tung
މਿᓾٙɚॴഐ࿴ཫ಻˙جdҢࡁɰ০࿁membrane
University (1980) Selected Publications
Selected Publications
proteinsආБɚॴഐ࿴€̍ўtopologyཫ಻d˸
• Honor: The Ten Outstanding Young Women Award,
ʿत֛ٙɚॴഐ࿴ආБཫ಻fҢࡁͦۃ˸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 predictionމਿᓾd೯࢝ɧॴഐ࿴ཫ಻f
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-
՘п͛يኪ࢕Ԙ஺ήҬՑ޴ᗫٙ˖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٫ʕᐏ੻ୋʬΤf 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˸ʿ͛يΤ൚ග޴ʝᗫڷʘ፫ᗆiԷνjஐ 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).
Τ൚ගᗫڷ፫ᗆٙ޼ӺdҢࡁԨ೯࢝͛يᗫڷٙႧ 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
ɢ׵೯࢝ІਗʷʱؓʈՈfҢࡁʊҁϓɓࢁΤމ 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-Qٙழ᜗ӻ୕dஈଣ˸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-
ழ᜗dᏐ͜׵ɽඎٙஐͣሯ᜗ኪʘ޼ӺiԨ೯࢝ൖ 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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