Soon Heng TAN (Chris)
Profile
Chris possesses a B.Sc (Honours, 2nd Upper) in Molecular Biology and M.Sc in Computer Science from National University of Singapore (NUS). His research interest is in bioinformatics and computational biology, in particular, in designing algorithms and computational approaches for knowledge discovery in biology. He is currently pursuing a Ph.D at the Department of Molecular Genetics and Microbiology in University of Toronto under the supervision of [http://pawsonlab.mshri.on.ca: Tony Pawson] and [:GaryBader:Gary Bader].
Contact
Email: chris.tan[at]utoronto.ca
Research Experience
Prior to commencing his Ph.D voyage at University of Toronto, Chris had the privilege of working with scientists in the fields of machine learning, knowledge discovery and data mining, natural language processing and text mining from [http://www.i2r.a-star.edu.sg: The Institute for Infocomm Research (Singapore)] and [http://www.comp.nus.edu.sg: School of Computing, National University of Singapore]. He is grateful to [http://www1.i2r.a-star.edu.sg/~xlli Xiaoli Li], [http://sdmc.i2r.a-star.edu.sg/~skng See-Kiong Ng], [http://www.comp.nus.edu.sg/~ksung Wing-Kin Sung] and [http://www.comp.nus.edu.sg/~wongls Limsoon Wong] for guiding his adventure in Computer Science.
Algorithm design for automating sequence motif discovery from protein interaction data
A correlated motif approach for finding short linear motifs from protein interaction networks
S.H. Tan, W. Hugo, W.K. Sung and S.K. Ng
- BMC Bioinformatics, 7:502 (2006)
Discovering novel interacting motif pairs from large protein-protein interaction datasets
S.H. Tan, W.K. Sung and S.K. Ng
- IEEE Fourth Symposium on Bioinformatics and Bioengineering (BIBE2004), Taichung, Taiwan, May 19-21, pp568-575.
Automated linear motif discovery from protein interaction network
- M.Sc Dissertation in Computer Science (2005)
Mining genome-wide interaction data for promiscuous signal motifs
S.H. Tan, W.K. Sung and S.K. Ng (2004) POSTER
The 5th HUGO Pacific Meeting & 6th Asia-Pacific Conference on Human Genetics. Singapore, August 17-20.
Elucidating the underlying domain structures and co-evolution between interacting proteins for interaction prediction/validation
Protein interaction prediction using inferred domain interactions and biologically-significant negative dataset
X.L. Li, S.H. Tan and S.K. Ng
- International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 1 Number 2
ADVICE: automated detection and validation of interaction by co-evolution
S.H. Tan, Z. Zhang and S.K. Ng
- Nucleic Acids Research, Vol. 32: W69-W72 (2004)
Integrative approach for computationally inferring protein domain interactions
S.K. Ng, Z. Zhang and S.H. Tan
- Bioinformatics 8: 923-929 (2003)
InterDom: a database of putative interacting protein domains for validating predicted protein interactions and complexes
S.K. Ng, Z. Zhang and S.H. Tan, K. Lin
- Nucleic Acids Research 31: 251-254 (2003)
Understanding the cell’s functional organization through network analysis and graph mining of protein interaction network
Interaction graph mining for protein complexes using local clique merging
X.L. Li, S.H. Tan, C.S. Foo and S.K. Ng
- The 16th International Conference on Genomic Informatics (GIW2005), in Genome Informatics 16(2): 260-269
Extraction of protein-protein interaction data from free text
Challenges in biological literature mining for online discovery of molecular interaction pathways
S.K. Ng and S.H. Tan
- International Journal of Computer Applications in Technology (IJCAT), Special Issue on "Data Mining Applications" (2007)
Recognition of protein and gene names from text using an ensemble of classifiers and effective abbreviation resolution
G. Zhou, D. Shen, J. Zhang, J. Su, S.H. Tan and C.L. Tan
- BMC Bioinformatics, 6 (Suppl 1):S7 (2005)
- A standard corpus for evaluating extraction of molecular interaction pathway information from scientific abstracts
S.H. Tan and S.K. Ng (2002) POSTER
- The 10th International Conference on Intelligent Systems for Molecular Biology, Edmonton, Canada, August 3-7.
Functional study of genes from expression data
On combining multiple microarray studies for improved functional classification by whole-dataset feature selection
S.K Ng, S.H. Tan and V.S. Sundararajan
- The 14th International Conference on Genomic Informatics (GIW2003), in Genome Informatics 14: 44-53
Chris's Log Book
[:/ToDo:To Dos]
[:/LiteratureReview: Literature Review]