Chris Soon Heng Tan
Chris Soon Heng Tan
Department of Chemistry, Southern University of Science and Technology
Verified email at
Cited by
Cited by
Integrative approach for computationally inferring protein domain interactions
SK Ng, Z Zhang, SH Tan
Bioinformatics 19 (8), 923-929, 2003
Genome of Acanthamoeba castellanii highlights extensive lateral gene transfer and early evolution of tyrosine kinase signaling
M Clarke, AJ Lohan, B Liu, I Lagkouvardos, S Roy, N Zafar, C Bertelli, ...
Genome biology 14 (2), R11, 2013
InterDom: a database of putative interacting protein domains for validating predicted protein interactions and complexes
SK Ng, Z Zhang, SH Tan, K Lin
Nucleic acids research 31 (1), 251-254, 2003
A mitotic phosphorylation feedback network connects Cdk1, Plk1, 53BP1, and Chk2 to inactivate the G2/M DNA damage checkpoint
MATM van Vugt, AK Gardino, R Linding, GJ Ostheimer, HC Reinhardt, ...
PLoS biology 8 (1), 2010
Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases
CSH Tan, B Bodenmiller, A Pasculescu, M Jovanovic, MO Hengartner, ...
Science signaling 2 (81), ra39-ra39, 2009
Interaction graph mining for protein complexes using local clique merging
XL Li, CS Foo, SH Tan, SK Ng
Genome Informatics 16 (2), 260-269, 2005
Recognition of protein/gene names from text using an ensemble of classifiers
GD Zhou, D Shen, J Zhang, J Su, SH Tan
BMC bioinformatics 6 (S1), S7, 2005
Proteome-wide drug and metabolite interaction mapping by thermal-stability profiling
KVM Huber, KM Olek, AC Müller, CSH Tan, KL Bennett, J Colinge, ...
Nature methods 12 (11), 1055-1057, 2015
Positive selection of tyrosine loss in metazoan evolution
CSH Tan, A Pasculescu, WA Lim, T Pawson, GD Bader, R Linding
Science 325 (5948), 1686, 2009
ADVICE: automated detection and validation of interaction by co-evolution
SH Tan, Z Zhang, SK Ng
Nucleic acids research 32 (suppl_2), W69-W72, 2004
A correlated motif approach for finding short linear motifs from protein interaction networks
SH Tan, W Hugo, WK Sung, SK Ng
BMC bioinformatics 7 (1), 502, 2006
Thermal proximity coaggregation for system-wide profiling of protein complex dynamics in cells
CSH Tan, KD Go, X Bisteau, L Dai, CH Yong, N Prabhu, MB Ozturk, ...
Science 359 (6380), 1170-1177, 2018
Functional centrality: detecting lethality of proteins in protein interaction networks
KL Tew, XL Li, SH Tan
Genome Informatics 2007: Genome Informatics Series Vol. 19, 166-177, 2007
A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain–peptide interaction from primary sequence
X Shao, CSH Tan, C Voss, SSC Li, N Deng, GD Bader
Bioinformatics 27 (3), 383, 2011
The RNA‐binding protein HuR/ELAVL1 regulates IFN‐β mRNA abundance and the type I IFN response
B Herdy, T Karonitsch, GI Vladimer, CSH Tan, A Stukalov, C Trefzer, ...
European journal of immunology 45 (5), 1500-1511, 2015
Sequence-specific recognition of a PxLPxI/L motif by an ankyrin repeat tumbler lock
C Xu, J Jin, C Bian, R Lam, R Tian, R Weist, L You, J Nie, A Bochkarev, ...
Science signaling 5 (226), ra39-ra39, 2012
Roles of “junk phosphorylation” in modulating biomolecular association of phosphorylated proteins?
CSH Tan, C Jĝrgensen, R Linding
Cell Cycle 9 (7), 1276-1280, 2010
Discovering protein–protein interactions
SK Ng, SH Tan
Journal of Bioinformatics and Computational Biology 1 (04), 711-741, 2004
Experimental and computational tools useful for (re) construction of dynamic kinase–substrate networks
CSH Tan, R Linding
Proteomics 9 (23), 5233-5242, 2009
Improving domain-based protein interaction prediction using biologically-significant negative dataset
XL Li, SH Tan, SK Ng
International Journal of Data Mining and Bioinformatics 1 (2), 138-149, 2006
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