Daifeng Wang, PhD

Position title: Assistant Professor, Biostatistics & Medical Informatics, Computer Sciences

PhD, University of Texas – Austin

Contact Information

Waisman Center
1500 Highland Avenue
Room 517
Madison, WI 53705
daifeng.wang@wisc.edu
Daifeng Lab

Research Statement

My research focuses on developing interpretable machine learning approaches and bioinformatics tools to integrate and analyze multi-omics data for understanding functional genomics and gene regulation in the human brain; e.g., he recently developed interpretable deep neural network modeling for single-cell deconvolution and genotype-phenotype prediction to reveal the molecular mechanisms and functional pathways in human brain disorders. My research has also been applied to comparative genomics; e.g., I designed novel comparative network clustering methods to uncover conserved and species-specific developmental gene regulatory networks and expression patterns across model organisms. I am currently working on deciphering the functional genomics for deep phenotypes across brain diseases such as neuropsychiatric and neurodegenerative diseases, aiming to discover the regulatory mechanisms and genomic engineering principles for precision medicine.

Selected Publications

Gao Y, Shen M, Gonzalez JC, Dong Q, Kannan S, Hoang JT, Eisinger BE, Pandey J, Javadi S, Chang Q, Wang D, Overstreet-Wadiche L, Zhao X. (2020). RGS6 Mediates Effects of Voluntary Running on Adult Hippocampal Neurogenesis. Cell Reports, 32(5):107997. doi: 10.1016/j.celrep.2020.107997.
Nguyen ND, Wang D. (2020). Multiview learning for understanding functional multiomics. PLoS Computational Biology, 16(4):e1007677. doi: 10.1371/journal.pcbi.1007677.
Yan KK, Wang D, Xiong K, Gerstein M. (2020). Comparing Technological Development and Biological Evolution from a Network Perspective. Cell Systems, 10(3):219-222. doi: 10.1016/j.cels.2020.02.004.
Nguyen ND, Blaby IK, Wang D. (2019). ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks. BMC Genomics. 2019 Dec 30;20(Suppl 12):1003. doi: 10.1186/s12864-019-6329-2.
Li M, Santpere G, Imamura Kawasawa Y, Evgrafov OV, Gulden FO, Pochareddy S, Sunkin SM, Li Z, Shin Y, Zhu Y, Sousa AMM, Werling DM, Kitchen RR, Kang HJ, Pletikos M, Choi J, Muchnik S, Xu X, Wang D, Lorente-Galdos B, Liu S, Giusti-Rodríguez P, Won H, de Leeuw CA, Pardiñas AF; BrainSpan Consortium; PsychENCODE Consortium; PsychENCODE Developmental Subgroup, Hu M, Jin F, Li Y, Owen MJ, O’Donovan MC, Walters JTR, Posthuma D, Reimers MA, Levitt P, Weinberger DR, Hyde TM, Kleinman JE, Geschwind DH, Hawrylycz MJ, State MW, Sanders SJ, Sullivan PF, Gerstein MB, Lein ES, Knowles JA, Sestan N. (2018). Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science, 362(6420). pii: eaat7615. doi: 10.1126/science.aat7615.
Gandal MJ, Zhang P, Hadjimichael E, Walker RL, Chen C, Liu S, Won H, van Bakel H, Varghese M, Wang Y, Shieh AW, Haney J, Parhami S, Belmont J, Kim M, Moran Losada P, Khan Z, Mleczko J, Xia Y, Dai R, Wang D, Yang YT, Xu M, Fish K, Hof PR, Warrell J, Fitzgerald D, White K, Jaffe AE; PsychENCODE Consortium, Peters MA, Gerstein M, Liu C, Iakoucheva LM, Pinto D, Geschwind DH. (2018). Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science, 362(6420). pii: eaat8127. doi: 10.1126/science.aat8127.
Wang D, Liu S, Warrell J, Won H, Shi X, Navarro FCP, Clarke D, Gu M, Emani P, Yang YT, Xu M, Gandal MJ, Lou S, Zhang J, Park JJ, Yan C, Rhie SK, Manakongtreecheep K, Zhou H, Nathan A, Peters M, Mattei E, Fitzgerald D, Brunetti T, Moore J, Jiang Y, Girdhar K, Hoffman GE, Kalayci S, Gümüş ZH, Crawford GE; PsychENCODE Consortium, Roussos P, Akbarian S, Jaffe AE, White KP, Weng Z, Sestan N, Geschwind DH, Knowles JA, Gerstein MB. (2018). Comprehensive functional genomic resource and integrative model for the human brain. Science, 362(6420). pii: eaat8464. doi: 10.1126/science.aat8464.
Wang D, He F, Maslov S, Gerstein M. (2016). 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. doi: 10.1371/journal.pcbi.1005146.
Wang D, Yan KK, Rozowsky J, Pan E, Gerstein M. (2016). Temporal Dynamics of Collaborative Networks in Large Scientific Consortia. Trends in Genetics, 32(5):251-253. doi: 10.1016/j.tig.2016.02.006.
Yan KK, Wang D, Sethi A, Muir P, Kitchen R, Cheng C, Gerstein M. (2016). Cross-Disciplinary Network Comparison: Matchmaking Between Hairballs. Cell Systems, 23;2(3):147-157.
Wang D, Yan KK, Sisu C, Cheng C, Rozowsky J, Meyerson W, Gerstein MB. (2015). Loregic: a method to characterize the cooperative logic of regulatory factors. PLoS Computational Biology, 11(4):e1004132. doi: 10.1371/journal.pcbi.1004132.
Comparative analysis of regulatory information and circuits across distant species. (2014). Boyle AP, Araya CL, Brdlik C, Cayting P, Cheng C, Cheng Y, Gardner K, Hillier LW, Janette J, Jiang L, Kasper D, Kawli T, Kheradpour P, Kundaje A, Li JJ, Ma L, Niu W, Rehm EJ, Rozowsky J, Slattery M, Spokony R, Terrell R, Vafeados D, Wang D, Weisdepp P, Wu YC, Xie D, Yan KK, Feingold EA, Good PJ, Pazin MJ, Huang H, Bickel PJ, Brenner SE, Reinke V, Waterston RH, Gerstein M, White KP, Kellis M, Snyder M. Nature, 512(7515):453-6. doi: 10.1038/nature13668.