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Dr. Jianwen Fang

Computational Biologist, Computational & Systems Biology Branch

Research Interests

Data-mining algorithms, systems biology, protein bioinformatics, software and database development, computational drug discovery, integrated data analysis.

Selected Publications

Fang J, Predicting thermostability difference between cellular protein orthologs, Bioinformatics, Volume 39, Issue 8, August 2023, btad504, https://doi.org/10.1093/bioinformatics/btad504

Fang J (2023) The role of data imbalance bias in the prediction of protein stability change upon mutation. PLOS ONE 18(3): e0283727. https://doi.org/10.1371/journal.pone.0283727

Coghill AE, Fang J, Liu Z, Chen CJ, Jarrett RF, Hjalgrim H, Proietti C, Yu KJ, Hsu WL, Lou PJ, Wang CP, Zhao Y, Doolan DL, Hildesheim A. Identifying Epstein-Barr virus peptide sequences associated with differential IgG antibody response. Int J Infect Dis. 2022 Jan;114:65-71. https://doi.org/10.1016/j.ijid.2021.10.054

Krushkal J, Negi S, Yee LM, Evans JR, Grkovic T, Palmisano A, Fang JW, Sankaran H, McShane LM, Zhao YD, O'Keefe BR. Molecular Genomic Features Associated with in Vitro Response of the NCI-60 Cancer Cell Line Panel to Natural Products. Molecular Oncology. 2021 Feb;15(2):381-406. https://doi.org/10.1002/1878-0261.12849

Fang JW*, A Critical Review of five Machine Learning Algorithms for Predicting Protein Stability Changes upon Mutations. Briefings in Bioinformatics (2019), bbz071. https://doi.org/10.1093/bib/bbz071

Evans DM, Fang JW, Silvers T, Delosh R, Laudeman J, Ogle C, Reinhart R, Selby M, Bowles L, Connelly J, Harris E, Krushkal J, Rubinstein L, Doroshow JH, Teicher BA*. EXPOSURE TIME VERSUS CYTOTOXICITY FOR ANTICANCER AGENTS, Cancer Chemother Pharmacol (2019) 84: 359. https://doi.org/10.1007/s00280-019-03863-w

Monks A, Zhao Y, Hose C, Hamed H, Krushkal J, Fang JW, Sonkin D, Palmisano A, Polley E, Fogli L, Konate M, Miller S, Simpson M, Voth A, Li M-C, Harris E, Wu X, Connelly J, Rapisarda A, Teicher B, Simon R, Doroshow JH*.The NCI Transcriptional Pharmacodynamics Workbench: a tool to examine dynamic expression profiling of therapeutic response in the NCI-60 cell line panel. Cancer Research, 78: 6807-6817, 2018. https://doi.org/10.1158/0008-5472.CAN-18-0989

Fang, JW*, Tightly Integrated Genomic and Epigenomic Data Mining Using Tensor Decomposition, Bioinformatics (2018), 35 (1), 112-118. https://doi.org/10.1093/bioinformatics/bty513

Luo D, Wang SL*, Fang JW, Zhang W. MIMPFC: Identifying miRNA-mRNA regulatory modules by combining phase-only correlation and improved rough-fuzzy clustering, Journal of bioinformatics and computational biology (2018), 16 (01), 1750028. https://doi.org/10.1142/S0219720017500287

Bender J, Fang JW, Simon R*. A computational study of the inhibition mechanisms of P-glycoprotein mediated paclitaxel efflux by kinase inhibitors. BMC Systems Biology (2017) 11:108. https://rdcu.be/bM29j (co-first author)

Wang Y*, Fang JW, Chen S. Inference of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties. Scientific Reports 6, Article number: 32679 (2016) (co-first author). https://www.nature.com/articles/srep32679

Saeedipour S, Tai D, Fang JW*: ChemCom: A Software Program for Searching and Comparing Chemical Libraries. Journal of Chemical Information and Modeling 55(7): 1292-1296 (2015). https://doi.org/10.1021/ci500713s

Zhan L, Liu HX, Fang Y, Kong B, He Y, Zhong X, Fang JW, Wan YJY*. Genome-Wide Binding and Transcriptome Analysis of Human Farnesoid X Receptor in Primary Human Hepatocytes. PloS one, 2014, 9 (9), e105930. https://doi.org/10.1371/journal.pone.0105930

Fang YP, Gao S, Tai D, Middaugh CR, Fang JW*. Identification of properties important to protein aggregation using feature selection. BMC Bioinformatics 2013, 14:314. https://doi.org/10.1186/1471-2105-14-314.

Dasouki MJ*, Rafi SK, Olm-Shipman AJO, Wilson NR, Abhyankar S, Ganter B, Furness LM, Fang JW, Calado RT, and Saadi I*. Exome sequencing reveals a thrombopoietin ligand mutation in a Micronesian family with autosomal recessive aplastic anemia. Blood 122, no. 20 (2013): 3440-3449. https://doi.org/10.1182/blood-2012-12-473538

Thomas AM, Hart SN, Li GD, Lu H, Fang YP, Fang JW, Zhong XB, and Guo GL*. Hepatocyte Nuclear Factor 4 Alpha and Farnesoid X Receptor Co-regulates Gene Transcription in Mouse Livers on a Genome-Wide Scale. Pharmaceutical research (2013): 1-11. https://doi.org/10.1007/s11095-013-1006-7

Fang YP, and Fang JW*. Discrimination of soluble and aggregation-prone proteins based on sequence information. Mol. BioSyst. 9, no. 4 (2013): 806-811. https://doi.org/10.1039/c3mb70033j

Thomas JC, O'Hara JM, Hu L, Gao FP, Joshi SB, Volkin DB, Robert N. Brey RN, Fang JW, Karanicolas J, Mantis NJ, and Middaugh CR*. Effect of single-point mutations on the stability and immunogenicity of a recombinant ricin chain subunit vaccine antigen. Human Vaccines & Immunotherapeutics 9:4, 1-9; April 2013. https://doi.org/10.4161/hv.22998

Fang YP, Liu HX, Zhang N, Guo G, Wan YY, Fang JW*. NURBS: A Database of Experimental and Predicted Nuclear Receptor Binding Sites. 2013 Jan 15; 29(2):295-7. https://dx.doi.org/10.1093%2Fbioinformatics%2Fbts693

Gao S, Xu S, Fang YP, Fang JW*. Prediction of Core Cancer Genes Using Multitask Classification Framework. J Theor Biol. 2012, 317, 62-70. https://doi.org/10.1016/j.jtbi.2012.09.027

Li Y, Fang JW*. PROTS-RF: A Robust Model for Predicting Mutation Induced Protein Stability Change. PLoSONE, 2012. 7(10): e47247. https://dx.doi.org/10.1371%2Fjournal.pone.0047247

Fang YP, Middaugh CR, Fang JW*. In silico classification of proteins from acidic and neutral cytoplasms. PLoSONE. 2012. 7(9): e45585. https://doi.org/10.1371/journal.pone.0045585

Tai D and Fang JW*. SymDex: Increasing the Efficiency of Chemical Fingerprint Similarity Searches for Comparing Large Chemical Libraries by Using Query Set Indexing. J. Chem. Inf. Model. 2012, 52 (8), 1926-1935. https://doi.org/10.1021/ci200606t

Wang SL, Li XL, Fang JW*. Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification. BMC Bioinformatics 2012, 13:178. https://doi.org/10.1186/1471-2105-13-178

Li Y, Zhang J, Tai D, Middaugh CR, Zhang Y, Fang JW*. PROTS: a Fragment Based Protein Thermo-Stability Potential. Proteins: Structure, Function, and Bioinformatics. 2012, 80, 1, 81-92. https://doi.org/10.1002/prot.23163

Li Y, Fang YP, Fang JW*. Predicting Residue-Residue Contacts Using Random Forest Models. Bioinformatics. 2011, 27(24), 3379-3384. https://doi.org/10.1093/bioinformatics/btr579

Tai D, Chaguturu R, Fang JW*. K-Screen: A Free Application for High Throughput Screening Data Analysis, Visualization, and Laboratory Information Management. Comb Chem High Throughput Screen. 2011, 14, 9, 757-765. https://doi.org/10.2174/138620711796957116

Fang JW*, Tai D. Evaluation of Mutual Information, Genetic Algorithm and SVR for Feature Selection in QSAR Regression. Curr Drug Discov Technol. 2011 Jun 1;8(2):107-11. https://doi.org/10.2174/157016311795563839

Cai S, Fang JW*, Li QS, Borchardt RT, Kuczera K, Middaugh CR, and Schowen RL. Comparative kinetics of cofactor association and dissociation for the human and trypanosomal S-adenosylhomocysteine hydrolases. 3. The role of lysyl and tyrosyl residues of the C-terminal extension. Biochemistry, 2010 Sep 28;49(38):8434-41. https://doi.org/10.1021/bi1007595

Li Y, Fang JW.* Distance-dependent statistical potentials for discriminating thermophilic and mesophilic proteins. Biochem. Biophys. Res. Commun. 396, 3, 736-741, 2010. https://dx.doi.org/10.1016%2Fj.bbrc.2010.05.005

Li Y, Middaugh CR, Fang JW.* A novel scoring function for discriminating hyperthermophilic and mesophilic proteins with application to predicting relative thermostability of protein mutants. BMC Bioinformatics. 2010 Jan 28;11(1):62. https://doi.org/10.1186/1471-2105-11-62

Thomas AM, Hart SN, Kong B, Fang JW, Zhong XB, Guo GL. Genome-wide tissue-specific farnesoid X receptor binding in mouse liver and intestine. Hepatology. 51, 4, 1410-1419, 2009. https://doi.org/10.1002/hep.23450

Fang JW, Koen YM, Hanzlik RP*. Bioinformatic analysis of xenobiotic reactive metabolite target proteins and their interacting partners. BMC Chem Biol. 2009 Jun 12;9(1):5. https://dx.doi.org/10.1186%2F1472-6769-9-5

Silva RA, Huang R, Morris J, Fang JW, Gracheva EO, Ren G, Kontush A, Jerome WG, Rye KA, Davidson WS. Structure of apolipoprotein A-I in spherical high density lipoproteins of different sizes. Proc Natl Acad Sci U S A. 2008 Aug 26;105(34):12176-81. https://doi.org/10.1073/pnas.0803626105

Hu C, Fang JW, Borchardt RT, Schowen RL, Kuczera K. Molecular dynamics simulations of domain motions of substrate-free S-Adenosyl-L-homocysteine hydrolase in solution. PROTEINS: Structure, Function, and Bioinformatics. 71(1), 131-143, 2008. https://doi.org/10.1002/prot.21664

Fang JW*, Dong YH, Salamat-miller N, Middaugh CR. DB-PABP: a database of polyanion binding proteins. Nucleic Acid Research. 36(Database issue):D303-D306, 2008. https://doi.org/10.1093/nar/gkm784

Salamat-Miller NS, Fang JW, Seidel C, Smalter A, Assenov Y, Albrecht M, Middaugh1 CR*. A Network-Based Analysis of Polyanion Binding Proteins Utilizing Yeast Protein Arrays. Molecular and Cellular Proteomics, 5, 12, 2263-2278, 2006. https://doi.org/10.1074/mcp.M600240-MCP200

Fang JW*, Dong YH, Lushington GH, Ye QZ, Georg GI. Support Vector Machines in HTS Data Mining: Type I MetAPs Inhibition Study. J. Biomolecular Screening 11 (2): 138-144, 2006. https://doi.org/10.1177/1087057105284334

Haasl RJ, Fang JW.* Mining protein-protein interaction data. Current Bioinformatics 1, 2: 197-205, 2006. (Invited review). https://doi.org/10.2174/157489306777011923

Fang, JW.*; Haasl, R.J.; Dong, Y.H.; Lushington, G. H. Discover protein sequence signatures from protein-protein interaction data. BMC Bioinformatics 6: 277, 2005. https://doi.org/10.1186/1471-2105-6-277

Marchin M, Kelly PT and Fang JW* Tracker: continuous HMMER and BLAST searching. Bioinformatics 21: 388-389, 2005. https://doi.org/10.1093/bioinformatics/bti012