• ChrisTan

Soon Heng TAN (Chris)

Profile

Chris possesses a B.Sc (Honours, 2nd Upper) in Molecular Biology and a 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 pursued a Ph.D at the Department of Molecular Genetics and Microbiology in University of Toronto under the supervision of Tony Pawson and Gary Bader from 2006 to 2011. For his Ph.D, he characterized the evolutionary dynamics of phosphorylation sites for functional phospho-proteomics and to understand the evolution of phosphorylation-based cell signaling. Presently, he is undergoing postdoctoral training in the laboratory of Guilio Superti-Furga at the Research Center for Molecular Medicine in Vienna, Austria where he investigates the molecular basis of oncogene/gene addiction in cancerous cells. In layman's terms, he is trying to understand how can some drugs kill cancer cells and not you.

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 The Institute for Infocomm Research (Singapore) and School of Computing, National University of Singapore. He is grateful to Xiaoli Li, See-Kiong Ng, Wing-Kin Sung and Limsoon Wong for guiding his adventure in Computer Science.

Contact

Email: chris.tan[at]utoronto.ca, ctan[at]cemm.oeaw.ac.at

Research Experience

Updated profile of my research is available at and Google Scholar

Signaling network evolution and functional phospho-proteomics

• A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain-peptide interaction from primary sequence

  • X. Shao, C.S.H Tan, C. Voss, S.S. Li, N. Deng, G.D. Bader

  • Bioinformatics (2010) doi: 10.1093/bioinformatics/btq657

• Roles of "junk phosphorylation" in modulating biomolecular association of phosphorylated proteins?

  • C.S.H. Tan, J. Claus, R. Linding PERSPECTIVE

  • Cell Cycle 9(7): 1276-80 (2010)

• A Mitotic Phosphorylation Feedback Network Connects Cdk1, Plk1, 53BP1, and Chk2 to Inactivate the G2/M DNA Damage Checkpoint

  • M. A. T. M. van Vugt, A. K. Gardino, R. Linding, G. J. Ostheimer, H. C. Reinhardt, S.-E Ong, C.S.H. Tan, H. Miao, S. M. Keezer, J. Li, T. Pawson, T. A. Lewis, S. A. Carr, S. J. Smerdon, T. R. Brummelkamp, M. B. Yaffe

  • PLoS Biol 8(1): e1000287. (2010)

• Positive Selection of Tyrosine Loss in Metazoan Evolution

  • C.S.H. Tan, A. Pasculescu, W.A. Lim, T. Pawson, G.D Bader, R. Linding

  • Science 325(5948):1686-8 (2009)

• Comparative Analysis Reveals Conserved Protein Phosphorylation Networks Implicated in Multiple Diseases

  • C.S.H. Tan, B. Bodenmiller, A. Pasculescu, M. Jovanovic, M.O. Hengartner, C. Jørgensen, G.D. Bader, R. Aebersold, T. Pawson, R. Linding

  • Science Signaling 2(81):r39 (2009)

• Experimental and computational tools useful for (re)construction of dynamic kinase-substrate networks

  • C.S.H. Tan and R. Linding REVIEW

  • Proteomics 9(23):5233-42 (2009)

• Understanding signaling network evolution: A quantitative approach

  • S.H. Tan, X. Shao, N. Deng, T. Pawson, R. Linding, G.D. Bader POSTER

  • The 2nd Annual q-bio Conference on Cellular Information Processing. Santa Fe, New Mexico, USA, August 6-9, 2008

• Functional and Evolutionary Analysis of Modular Protein Interaction Domains

  • S.H. Tan, T. Pawson and G.D. Bader POSTER

  • FEBS Workshop: The Biology of Modular Protein Domain. Seefeld, Tirol, Austria, September 8-13, 2007

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, 2004, 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 POSTER

  • The 5th HUGO Pacific Meeting & 6th Asia-Pacific Conference on Human Genetics. Singapore, August 17-20, 2004

Elucidating the underlying domain structures and co-evolution between interacting proteins for interaction prediction/validation

• PPiClust: Efficient clustering of 3D protein-protein interaction interfaces

  • Z. Aung, S.H. Tan, S.K. Ng and K.L. Tan

  • J Bioinform Comput Biol., 6(3):415-433 (2008)

• 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

• Functional centrality: detecting lethality of proteins in protein interaction networks

  • K.L Tew, X.L Li and S.H. Tan

  • The 18th International Conference on Genomic Informatics (GIW2007), in Genome Informatics 19: 166-177 (2007)

• 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 (2005)

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 and S.H. 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 POSTER

  • The 10th International Conference on Intelligent Systems for Molecular Biology, Edmonton, Canada, August 3-7, 2002

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 (2003)