Daifeng Wang

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:
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Chirag Gupta, Pramod Chandrashekar, Chenfeng He, Ting Jin, Saniya Khullar, Qiang Chang, Daifeng Wang, Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases, Journal of Neurodevelopmental Disorders, 14, 28, 2022

Nam D Nguyen, Jiawei Huang, Daifeng Wang, A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data, Nature Computational Science, 2, 38–46, 2022 *

Jiawei Huang, Jie Sheng, Daifeng Wang, Manifold learning analysis suggests strategies for aligning single-cell multi-modalities and revealing functional genomics for neuronal electrophysiology, Communications Biology, 4, 1308, 2021 *

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, 13, 95, 2021 *

Nam D Nguyen, Ting Jin, Daifeng Wang, Varmole: A biologically drop-connect deep neural network model for prioritizing disease risk variants and genes, Bioinformatics, 37 (12), 1772-1775, 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, 37 (8), 1115-1124, 2021

Nam D Nguyen, Daifeng Wang, Multi-view learning for understanding functional multiomics, PLoS Computational Biology, 16(4): e1007677, 2020 *

Daifeng Wang, Shuang Liu, …, PsychENCODE Consortium, Nenad Sestan, Andrew E. Jaffe, Kevin White, Zhiping Weng, Daniel H. Geschwind, James Knowles, Mark Gerstein, Comprehensive functional genomic resource and integrative model for the human brain, Science, 362, 1266, 2018