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MaxiQ, and Ideal-Q), which has received a lot of attention and us- similarity, it was demonstrated that SymAlign has considerably
ers. We have also developed machine learning and knowledge- greater precision than certain other aligners (40% compared to
based algorithms for protein structure and function prediction, 6% or less for the others). Protein synonyms have also proven to
as well as protein interaction interface prediction. By regarding be very useful for predicting protein subcellular localization sites.
protein sequences as a language and identifying protein syno- Our next step is to explore the existence of grammatical patterns
nyms from a sizeable database, we developed a new sequence in protein sequences.
aligner, called SymAlign, to improve sequence alignments.
In testing several large datasets of protein pairs for structural

Publications

1. W. L. Hsu and R. McConnell, “PC-trees and circular-ones ar- 8. Chih-Chiang Tsou, Yin-Hao Tsui, Yi-Hwa Yian, Yi-Ju Chen,
rangements” Theoretical Computer Science, 296(1), 99-116, Chuan-Yih Yu, Han-Yin Yang, Ke-Shiuan Lynn, Yu-Ju Chen,
(2003). Ting-Yi Sung*, and Wen-Lian Hsu*, “MaXIC-Q Web: A Fully
Automated Web Service Using Statistical and Computational
2. W. F. Lu and W. L. Hsu. “A Test for the Consecutive Ones
Methods for Protein Quantitation Based on Stable Isotope
Property on Noisy Data - Application to Physical Mapping
Labeling and LC-MS,” Nucleic Acids Research 37, W661-
and Sequence Assembly,” Journal of Computational Biology
W669, (2009).
10(5), 709-735, (2004).
9. Chih-Chiang Tsou, Chia-Feng Tasi, Ying-Hao Tsui, Putty-
3. K. P. Wu, H. N. Lin, J. M. Chang, T. Y. Sung, and W. L. Hsu, Reddy Sudhir, Yi-Ting Wang, Yu-Ju Chen, Jeou-Yuan Chen,
“HYPROSP: A Hybrid Protein Secondary Structure Predic- Ting-Yi Sung, and Wen-Lian Hsu, “IDEAL-Q: An automated
tion Algorithm - a Knowledge-Based Approach,” Nucleic Ac- tool for label-free quantitation analysis using an efficient pep-

ids Research 32(17), 5059-5065, (2004).
tide alignment approach and spectral data validation,” Molec-
4. Hsin-Nan Lin, Jia-Ming Chang, Kuen-Pin Wu, Ting-Yi Sung ular & Cellular Proteomics 9, 131-144, (2010).
and Wen-Lian Hsu, “HYPROP II: A knowledge-based hybrid 10. Hong-Jie Dai, Yen-Ching Chang, Richard Tzong-Han Tsai*,
method for protein secondary structure prediction based on and Wen-Lian Hsu*, “Integration of Gene Normalization

local prediction confidence,” Bioinformatics 21, 3227-3233, Stages and Co-reference Resolution Using a Markov-Logic
(2005).
Network,” Bioinformatics 27, 2586-2594, (2011).
5. Kuen-Pin Wu, Jia-Ming Chang, Jun-Bo Chen, Chi-Fon Chang,
11. 11. Mike Tian-Jian Jiang, Tsung-Hsien Lee, Wen-Lian Hsu,
Wen-Jin Wu, Tai-Huang Huang, Ting-Yi Sung and Wen-Lian
“The Left and Right Context of a Word: Overlapping Chinese
Hsu*, “RIBRA-an Error-Tolerant Algorithm for the NMR
Syllable Word Segmentation with Minimal Context,” ACM
Backbone Assignment Problem,” Journal of Computational
Transaction on Asian Language Infor-mation Processing,
Biology 13, 229-244, (2006).
11(4), 17:1-12:21, (2012).
6. Chuan-Yih Yu, Yin-Hao Tsui, Yi-Hwa Yian, Ting-Yi Sung, 12. 12. Chia-Ying Cheng, Chia-Feng Tsai, Yu-Ju Chen, Ting-
and Wen-Lian Hsu*, “The Multi-Q Web Server for Multi- Yi Sung, and Wen-Lian Hsu, “A spectrum-based method to
plexed Protein Quantitation,” Nu-cleic Acids Research.35, generate good decoy libraries for spectral library searching
W707-712, (2007).
in peptide identifications,” Journal of Proteome Research

7. Hong-Jie Dai, Chi-Hsin Huang, Ryan T. K. Lin, Richard 3;12(5):2305-10, (2013).
Tzong-Han Tsai, and Wen-Lian Hsu, “BIOSMILE web search:
a web application for annotating biomedical entities and rela-
tions,” Nucleic Acids Research 36, W390W398, (2008).

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