Daifeng Wang

Assistant Professor

daifeng.wang@wisc.edu

608-262-8567

517 Waisman Center

Research Interests
My research focuses on developing interpretable machine learning approaches and bioinformatics tools to analyze multi-omics data for understanding functional genomics and molecular mechanisms in the complex biological systems. With applications to human diseases, I aim to discover the regulatory mechanisms and genomic engineering principles for precision medicine. In particular, my current interests include but not limited to: · Single cell functional genomics and gene regulation · Neuropsychiatric, Neurodevelopmental and Neurodegenerative diseases · Deep neural network modeling for genotype-phenotype prediction · Representation learning for biological networks · Comparative genomics
Selected Publications
Selected Publications: (*co-first-author, equal contribution; # corresponding author) § Daifeng Wang*, Shuang Liu*, …, Daniel H. Geschwind, James Knowles, Mark Gerstein, Comprehensive functional genomic resource and integrative model for the human brain, Science, 362, 1266, 2018 § Michael J Gandal, Pan Zhang, …, Daifeng Wang, …, Daniel H. Geschwind, Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder, Science, 362, 1265, 2018 § Mingfeng Li*, Gabriel Santpere*, …, Daifeng Wang, …, Nenad Sestan, Integrative Functional Genomic Analysis of Human Brain Development and Neuropsychiatric Risk, Science, 362, 1264, 2018 § PsychENCODE consortium including Daifeng Wang, Revealing the brain’s molecular architecture, Science, 2018 Dec 14;362(6420):1262-1263 § Adam P Arkin, Robert W Cottingham, …, Daifeng Wang, Fangfang Xia, Hyunseung Yoo, Shinjae Yoo, Dantong Yu, KBase: The United States Department of Energy Systems Biology Knowledgebase, Nature Biotechnology, 36(7):566-569, 2018 § Pedro Alves, Shuang Liu, Daifeng Wang# and Mark Gerstein#, Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(3):926-933, 2018 § Zongdong Li, Natasha M. Nesbitt, Lisa E.Malone, Dimitri V. Gnatenko, Song Wu, Daifeng Wang, Wei Zhu, Geoffrey D. Girnun, Wadie F. Bahou, Heme degradation enzyme biliverdin IXB reductase is required for stem cell glutamine metabolism, Biochemical Journal, 475(6):1211-1223, 2018 § Daifeng Wang#, John D. Haley and Patricia Thompson#, Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages, BMC Cancer, 17:830, 2017 § Daifeng Wang, Fei He, Sergei Maslov, Mark Gerstein, DREISS: Using state-space models to infer the dynamics of gene expression driven by external and internal regulatory networks, PLoS Computational Biology, 12(10): e1005146, 2016 § Daifeng Wang, Koon-Kiu Yan, Joel Rozowsky, Eric Pan, Mark Gerstein, Temporal dynamics of collaborative networks driven by large scientific consortia, Trends in Genetics, 32, 251-253, 2016 § Koon-Kiu Yan*, Daifeng Wang*, Anurag Sethi, Robert Kitchen, Paul Muir, Chao Cheng, Mark Gerstein, Matchmaking hairballs – insights from cross-disciplinary network comparison, Cell Systems, 2, 147-157, 2016 (featured front-matter) § Paul Muir, Shantao Li, Shaoke Lou, Daifeng Wang, Daniel Spakowicz, Leonidas Salichos, Jing Zhang, Farren Isaacs, George M. Weinstock, Joel Rozowsky, Mark Gerstein, The real cost of sequencing: scaling computation to keep pace with data generation, Genome Biology, 17:53, 2016 § Fei He, Shinjae Yoo, Daifeng Wang, Sunita Kumari, Mark Gerstein, Doreen Ware, Sergei Maslov, Large-scale atlas of microarray data reveals biological landscape of gene expression in Arabidopsis, The Plant Journal, 86(6), 472-480, 2016 § The PsychENCODE Consortium including Daifeng Wang, The PsychENCODE Project Consortium, Nature Neuroscience, 18, 1707-1712, 2015 § Daifeng Wang, Koon-Kiu Yan, Cristina Sisu, Chao Cheng, Joel Rozowsky, William Meyerson, Mark Gerstein, Loregic: A method to characterize the cooperative logic of regulatory factors, PLoS Computational Biology 11(4): e1004132, 2015 (featured article) § Chao Cheng, Erik Andrews, Koon-Kiu Yan, Matthew Ung, Daifeng Wang, Mark Gerstein, An Approach for Determining and Measuring Network Hierarchy: Application to Comparing the Phosphorylome and the Regulome, Genome Biology, 16:63, 2015 § Shuang Liu, Anjali Datta, Derek Ho, Jordan Dwelle, Daifeng Wang, Thomas E. Milner, H. Grady Rylander III, Mia K. Markey, Effect of image registration on longitudinal analysis of retinal nerve fiber layer thickness of non-human primates using Optical Coherence Tomography (OCT), BMC Eye and Vision, 2:3, 2015 § Mark Gerstein*, Joel Rozowsky*, Koon-Kiu Yan*, Daifeng Wang*, Chao Cheng*, …, Steven Brenner, Brenton Graveley, Susan Celniker, Thomas Gingeras, and Robert Waterston, Comparative Analysis of the Transcriptome across Distant Species, Nature 512, 445–448, 2014 § Alan P. Boyle*, Carlos L. Araya*, …, Daifeng Wang, …, Elise A. Feingold, Peter J. Good, Michael J. Pazin, Haiyan Huang, Peter J. Bickel, Steven E. Brenner, Valerie Reinke, Robert H. Waterston, Mark Gerstein, Kevin P. White, Manolis Kellis, Michael Snyder, Comparative analysis of regulatory information and circuits across diverse species, Nature 512, 453–456, 2014 § Koon-Kiu Yan*, Daifeng Wang*, Joel Rozowsky, Henry Zheng, Mark Gerstein, OrthoClust: An orthology-based network framework for expression clustering across multiple species, Genome Biology 15:R100, 2014 § Cristina Sisu, Baikang Pei, Jing Leng, Adam Frankish, Yan Zhang, Suganthi Balasubramanian, Rachel Harte, Daifeng Wang, Michael Rutenberg Schoenberg, Wyatt Clark, Mark Diekhans, Joel Rozowsky, Tim Hubbard, Jennifer Harrow, Mark Gerstein, Comparative analysis of pseudogenes across three phyla, Proceedings of the National Academy of Sciences (PNAS), vol. 111, no. 37, pp. 13361–13366, 2014
Website
https://daifengwanglab.org/

Dr. Daifeng Wang