Daifeng Wang

Position title: Assistant Professor

Email: daifeng.wang@wisc.edu

Phone: (608) 262-8567

Address:
Biostatistics and Medical Informatics
Gene regulatory networks, functional genomics, neurogenomics, genotype-phenotype prediction, single-cell genomics

Address
517 Waisman Center
Education
Ph.D. The University of Texas at Austin (2011), Postdoctoral Research: Yale University (2012-2016)
Lab Website
https://daifengwanglab.org/
Research Fields
Bioinformatics, Computational systems biology, Network biology, Synthetic biology

Research Description:
Our fundamental understanding of the underlying molecular mechanisms from non-coding variants to phenotypes, especially at the cell-type level, remains elusive. The increasing amount of multi-omics data allows a better understanding of molecular and cellular mechanisms in phenotypes. However, analyzing and interpreting such large-scale omics data is challenging. To address this, my research develops computational approaches and bioinformatics tools for multi-omics data to understand complex functional genomics and gene regulation with applications to human diseases and model organisms. In particular, my lab currently works on the following areas: (1) Multi-omics data analysis to identify functional genomic elements and gene regulatory networks; (2) Comparative network analysis identifies functional structures across multiple high-dimensional biological networks; (3) Interpretable machine learning approaches for revealing cellular and molecular mechanisms from genotype to phenotype; (4) Identification of design and engineering principles in gene regulation for genome editing and synthetic biology.

Representative Publications:
Search PubMed for more publications by Daifeng Wang

Ting Jin, Peter Rehani, Mufang Ying, Jiawei Huang, Shuang Liu, Panos Roussos, Daifeng Wang, scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks, Genome Medicine, 2021

Ting Jin, Nam D Nguyen, Flaminia Talos, Daifeng Wang, ECMarker: Interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages, Bioinformatics, 2020

Nam D Nguyen, Ting Jin, Daifeng Wang, Varmole: A biologically drop-connect deep neural network model for prioritizing disease risk variants and genes, Bioinformatics, 2020

Nam D Nguyen, Daifeng Wang, Multi-view learning for understanding functional multiomics, PLoS Computational Biology, 2020

Nam D Nguyen, Ian K Blaby, Daifeng Wang, ManiNetCluster: A Manifold Learning Approach to Reveal the Functional Linkages Across Multiple Gene Networks, BMC Genomics, 2019

Daifeng Wang et al., Comprehensive functional genomic resource and integrative model for the human brain, Science, 2018