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

[http://www.google.com/calendar/embed?src=h2hoq53lpig6gh8etg7ai1r558%40group.calendar.google.com&pvttk=c3e8bc89cf0a72313f0b5e5ee97c30f3 My Schedule]

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] and for guiding his adventure in Computer Science.

Algorithm design for automated 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

  • BIBE 2004, Taichung, Taiwan, May 19-21, pp568-575.

• Automated linear motif discovery from protein interaction network

  • M.Sc Dissertation in Computer Science (2005)

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

  • Lectures Notes in Computer Science, 3482: pp318 (2005)

• 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

  • 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)

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

  • GIW2003, in Genome Informatics 14: 44-53

Chris's Log Book

• [:/ToDo:To Dos]

• [:/LiteratureReview: Literature Review]