Publications

  • Journal Papers
  • Invention Patent

Referred research publications in biomedical journals

 

  1. Y.T. Lim, N. Prabhu, L. Dai, K.D. Go, D. Chen, L. Sreekumar, L. Egeblad, S. Eriksson, L. Chen, S. Veerappan, H.L. Teo, C.S.H. Tan, J. Lengqvist, A. Larsson, R.M. Sobota, P. Nordlund (2018). An efficient proteome-wide strategy for discovery and characterization of cellular nucleotide-protein interactions. PLOS ONE 12 (12):e0208273.
  2. C.S.H. Tan#, K.D. Go, X. Bisteau, L. Dai, C.H. Yong, N. Prabhu, M.B. Ozturk, Y.T. Lim, L. Sreekumar, J. Lengqvist, V. Tergaonkar, P. Kaldis, R.M. Sobota, P. Nordlund (2018). Thermal Proximity Co-aggregation for System-wide Profiling of Protein Complex Dynamics in Cells. Science. 359 (6380):1170-1177.
    • Highlighted in Science 359 (6380):1105-1106, Nature Method 15 (4):242-243, Cell Systems 6 (3), F1000Prime, GenomeWeb (9th Feb 2018), PHYS.ORG and Nature INDEX.
  3. K. V. Huber, K.M. Olek, A.C. Müller, C.S.H Tan, K.L. Bennett, J. Colinge, G. Superti-Furga (2015). Proteome-wide drug and metabolite interaction mapping by thermal-stability profiling. Nat Methods. 12 (11): 1055-1057.
  4. B. Herdy, T. Karonitsch, G.I. Vladimer, C.S.H Tan, A. Stukalov, C. Trefzer, J.W. Bigenzahn, T. Theil, J. Holinka, H.P. Kiener, J. Colinge, K.L. Bennett, G. Superti-Furga (2015). The RNA-binding protein HuR/ELAVL1 regulates IFN-β mRNA abundance and the type I IFN response. Eur J Immunol. 45(5):1500-11.
  5. M. Clarke et. al. (2013). Genome of Acanthamoeba castellanii highlights extensive lateral gene transfer and early evolution ­of tyrosine kinase signaling. Genome Biology 14:R11
  6. Xu, J. Jin, C. Bian, R. Lam, R. Tian, R. Weist, L. You, J. Nie, A. Bochkarev, W. Tempel, C.S.H. Tan, G.A. Wasney, M. Vedadi, G.D. Gish, C.H. Arrowsmith, T. Pawson, X.J. Yang, J. Min (2012). Sequence-Specific Recognition of a PxLPxI/L Motif by an Ankyrin Repeat Tumbler Lock. Sci Signal. 5 (226):ra39
  7. C.S.H. Tan#, G.D. Bader (2012). Phosphorylation sites of higher stoichiometry are more conserved. Nat Methods. 9(4):317
  8. X. Shao*, C.S.H. Tan*, C. Voss, S.S.C. Li, N. Deng, G.D. Bader (2011). A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain-peptide interaction from primary sequences. Bioinformatics 27 (3): 383-390
  9. 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 (2010). A mitotic phosphorylation feedback network connects Cdk1, Plk1, 53BP1, and Chk2 to inactivate the G2/M DNA damage checkpoint. PLoS Biol. 8 (1): e1000287
  10. C.S.H. Tan*, B. Bodenmiller*, A. Pasculescu, M. Jovanovic, M.O. Hengartner, C. Jørgensen, G.D. Bader, R. Aebersold, T. Pawson, R. Linding (2009). Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases. Sci. Signal. 2 (81): r39
  11. C.S.H. Tan, A. Pasculescu, W.A. Lim, T. Pawson, G.D Bader, R. Linding (2009). Positive selection of tyrosine loss in metazoan evolution. Science 325 (5948): 1686-8
    • Highlighted in Nature Genetic Review 10 (9): 594, Science Signaling 3 (103), ACS Chemical Biology 4 (8): 595 and F1000Prime. Further work was published in Science 332 (6032): 917 as a response to a technical comment rose on the original work.
  12. Z. Aung, S.H. Tan, S.K. Ng and K.L. Tan (2008). PPiClust: Efficient clustering of 3D protein-protein interaction interfaces. J. Bioinform. Comput. Biol. 6 (3): 415-33
  13. S.H. Tan, W. Hugo, W.K. Sung and S.K. Ng (2006). A correlated motif approach for finding short linear motifs from protein interaction networks. BMC Bioinformatics 7: 502
  14. S.H. Tan*, Z. Zhang* and S.K. Ng (2004). ADVICE: automated detection and validation of interaction by co-evolution. Nucleic Acids Research 32: W69-W72
  15. G. Zhou, D. Shen, J. Zhang, J. Su, S.H. Tan and C.L. Tan (2004). Recognition of protein and gene names from text using an ensemble of classifiers and effective abbreviation resolution. BMC Bioinformatics 6 (Suppl 1): S7
  16. S.K. Ng, Z. Zhang and S.H. Tan (2003). Integrative approach for computationally inferring protein domain interactions. Bioinformatics 8: 923-929
  17. S.K. Ng, Z. Zhang and S.H. Tan, K. Lin (2003). InterDom: a database of putative interacting protein domains for validating predicted protein interactions and complexes. Nucleic Acids Research 31: 251-254

 

Referred research publications in computer science conferences

 

  1. Z. Aung, S.H. Tan, S.K. Ng and K.L. Tan (2007). Uncovering the structural basis of protein interactions with efficient clustering of 3-D interaction interfaces. CSB 2007, San Diego, California, USA, Aug 13-17, pp 287-97.
  2. K.L Tew, X.L Li and S.H. Tan (2007). Functional centrality: detecting lethality of proteins in protein interaction networks. GIW 2007, in Genome Inform. 19: 166-77
  3. X.L. Li, S.H. Tan, and S.K. Ng (2006). Improving domain-based protein interaction prediction using biologically-significant negative dataset. International Journal of Data Mining and Bioinformatics (IJDMB) 1(2): 138-149
  4. X.L. Li, S.H. Tan and S.K. Ng (2005). Protein interaction prediction using inferred domain interactions and biologically-significant negative dataset. ICCSA 2005, Singapore, May 9-12, in Lectures Notes in Computer Science 3482: 318-326
  5. X.L. Li, S.H. Tan, C.S. Foo and S.K. Ng (2005). Interaction graph mining for protein complexes using local clique merging. GIW 2005, in Genome Inform. 16 (2): 260-269
  6. X.L. Li, S.H. Tan, and S.K. Ng (2005). Exploring cross-function domain interaction map. BIOINFO 2005, Busan, Korea, Sept 22-24, 2005, pp 431-436
  7. S.H. Tan, W.K. Sung and S.K. Ng (2004). An automated approach for protein motif discovery using interaction-driven motif mining. ICCSA 2004, San Diego, USA, Jun 28-30, pp 224-232.
  8. S.H. Tan, W.K. Sung and S.K. Ng (2004). Discovering novel interacting motif pairs from large protein-protein interaction datasets. BIBE 2004, Taichung, Taiwan, May 19-21, pp 568-575.
  9. H.Q. Li, J. Li, S.H. Tan and S.K. Ng (2004). Discovery of binding motif pairs from protein complex structural data and protein interaction sequence data. PSB 2004, Big Island of Hawaii, Hawaii, USA, Jan 6-10, pp 312-23
  10. G. Zhou, D. Shen, J. Zhang, J. Su, S.H. Tan and C.L. Tan (2003). Recognition of Protein/Gene Names from Text using an Ensemble of Classifiers and Effective Abbreviation Resolution. BioCreAtIvE I, Granada, Spain, Mar 28-31, 2004, pp 26-30
  11. S.K. Ng, Z. Zhang and S.H. Tan (2003). Integrative approach for computationally inferring protein domain interactions. SAC 2003, Melbourne, FL, USA, Mar 9-12, pp 115-121
  12. S.K Ng, S.H. Tan and V.S. Sundararajan (2003). On combining multiple microarray studies for improved functional classification by whole-dataset feature selection. GIW 2003, in Genome Inform. 14: 44-53

 

Other scientific publications

 

  1. C.S.H Tan (2017). Databases and Computational Tools for Evolutionary Analysis of Protein Phosphorylation. Methods Mol Biol. 1636:475-484. BOOK CHAPTER
  2. P. Creixell, E.M. Schoof, C.S.H Tan, R. Linding (2012). Mutational properties of amino acid residues: implications for evolvability of phosphorylatable residues. Philos Trans R Soc Lond B Biol Sci. 367(1602):2584-93. doi: 10.1098/rstb.2012.0076 PERSPECTIVE
  3. C.S.H. Tan (2011). Sequence, structure and network evolution of protein phosphorylation. Sci. Signal. 4 (182): mr6 MEETING REPORT
  4. C.S.H. Tan, C. Jørgensen, R. Linding (2010). Roles of “junk phosphorylation” in modulating biomolecular association of phosphorylated proteins? Cell Cycle 9 (7): 1276-1280 PERSPECTIVE
  5. C.S.H. Tan & R. Linding (2009). Experimental and computational tools useful for (re)construction of dynamic kinase-substrate networks. Proteomics 9 (23): 5233-42 REVIEW
  6. S.K. Ng & S.H. Tan (2004). Discovering protein-protein interactions. J. Bioinform. Comput. Biol. 1 (4): 711-41 REVIEW
  7. S.H. Tan & S.K. Ng (2004). Discovering Protein-Protein Interactions, in The Practical Bioinformatician, Chapter 13, pages 293-318, World Scientific Publishing BOOK CHAPTER
  8. S.K. Ng and S.H. Tan (2006). Challenges in biological literature mining for online discovery of molecular interaction pathways. International Journal of Computer Applications in Technology (IJCAT) 27 (4): 259-269 REVIEW

* denotes co-first-author, # denotes co-corresponding author

  1. Soon Heng Tan, (WO2019035773)Methods to Identify Protein Interaction. PCT/SG2018/050422

Copyright © 2018 All Rights Reserved.