Chris Soon Heng Tan
Chris Soon Heng Tan
Department of Chemistry, Southern University of Science and Technology
Verified email at
Cited by
Cited by
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), 1-15, 2013
Integrative approach for computationally inferring protein domain interactions
SK Ng, Z Zhang, SH Tan
Bioinformatics 19 (8), 923-929, 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), e1000287, 2010
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
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
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
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
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 (1), 1-7, 2005
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), 1-16, 2006
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
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
Mutational properties of amino acid residues: implications for evolvability of phosphorylatable residues
P Creixell, EM Schoof, CSH Tan, R Linding
Philosophical Transactions of the Royal Society B: Biological Sciences 367 …, 2012
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
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
An efficient proteome-wide strategy for discovery and characterization of cellular nucleotide-protein interactions
YT Lim, N Prabhu, L Dai, KD Go, D Chen, L Sreekumar, L Egeblad, ...
PloS one 13 (12), e0208273, 2018
Roles of “junk phosphorylation” in modulating biomolecular association of phosphorylated proteins?
CSH Tan, C Jørgensen, 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
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