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

Javadi, S., Li, Y., Sheng, J., Zhao, L., Fu, Y., Wang, D., & Zhao, X. (2022). Sustained correction of hippocampal neurogenic and cognitive deficits after a brief treatment by Nutlin-3 in a mouse model of fragile X syndrome. BMC medicine, 20(1), 163. https://doi.org/10.1186/s12916-022-02370-9
Gupta, C., Chandrashekar, P., Jin, T., He, C., Khullar, S., Chang, Q., & Wang, D. (2022). Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases. Journal of neurodevelopmental disorders, 14(1), 28. https://doi.org/10.1186/s11689-022-09438-w
Fathi, A., Mathivanan, S., Kong, L., Petersen, A. J., Harder, C., Block, J., Miller, J. M., Bhattacharyya, A., Wang, D., & Zhang, S. C. (2022). Chemically induced senescence in human stem cell-derived neurons promotes phenotypic presentation of neurodegeneration. Aging cell, 21(1), e13541. https://doi.org/10.1111/acel.13541
Lin, C. X., Li, H. D., Deng, C., Liu, W., Erhardt, S., Wu, F. X., Zhao, X. M., Guan, Y., Wang, J., Wang, D., Hu, B., & Wang, J. (2022). An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer’s disease. Briefings in bioinformatics, 23(1), bbab522. https://doi.org/10.1093/bib/bbab522
Nguyen, N. D., Huang, J., & Wang, D. (2022). A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data. Nature computational science, 2(1), 38–46. https://doi.org/10.1038/s43588-021-00185-x
Huang, J., Sheng, J., & Wang, D. (2021). Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics. Communications biology, 4(1), 1308. https://doi.org/10.1038/s42003-021-02807-6
Nguyen, N. D., Jin, T., & Wang, D. (2021). Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes. Bioinformatics (Oxford, England), 37(12), 1772–1775. https://doi.org/10.1093/bioinformatics/btaa866
Jin, T., Rehani, P., Ying, M., Huang, J., Liu, S., Roussos, P., & Wang, D. (2021). scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks. Genome medicine, 13(1), 95. https://doi.org/10.1186/s13073-021-00908-9
Jin, T., Nguyen, N. D., Talos, F., & Wang, D. (2021). ECMarker: interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages. Bioinformatics (Oxford, England), 37(8), 1115–1124. https://doi.org/10.1093/bioinformatics/btaa935
Shen, M., Guo, Y., Dong, Q., Gao, Y., Stockton, M. E., Li, M., Kannan, S., Korabelnikov, T., Schoeller, K. A., Sirois, C. L., Zhou, C., Le, J., Wang, D., Chang, Q., Sun, Q. Q., & Zhao, X. (2021). FXR1 regulation of parvalbumin interneurons in the prefrontal cortex is critical for schizophrenia-like behaviors. Molecular psychiatry, 10.1038/s41380-021-01096-z. Advance online publication. https://doi.org/10.1038/s41380-021-01096-z
Huang, K., Wu, Y., Shin, J., Zheng, Y., Siahpirani, A. F., Lin, Y., Ni, Z., Chen, J., You, J., Keles, S., Wang, D., Roy, S., & Lu, Q. (2021). Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder. PLoS genetics, 17(2), e1009309. https://doi.org/10.1371/journal.pgen.1009309
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.
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.