Daifeng Wang
Position title: Associate Professor
Email: daifeng.wang@wisc.edu
Website: Daifeng Wang lab
Phone: 608-262-8567
Address:
Waisman Center, Room 517
1500 Highland Ave
Madison, WI 53705
- Research Interests
- My lab focuses on developing machine learning approaches and bioinformatics tools to analyze multimodal data for (1) improving genotype-phenotype prediction and (2) understanding functional genomics and gene regulation in the human brains and brain diseases. The current research topics include bio-inspired machine learning, single-cell functional genomics, multimodal integration and imputation.
- Website
- https://daifengwanglab.org/
- UW-Madison Affiliation
- Affiliate Professor, Department of Computer Sciences; Computer, Data & Information Sciences; College of Letters and Science
Selected Publications: (*co-first-author, equal contribution; # corresponding author)
- Sayali Alatkar, Daifeng Wang, CMOT: Cross Modality Optimal Transport for multimodal inference, accepted, Genome Biology, 2023
- Noah Cohen Kalafut, Xiang Huang, Daifeng Wang, Joint Variational Autoencoders for Multi-Modal Imputation and Embedding, Nature Machine Intelligence, 2023
- Chenfeng He, Noah Cohen Kalafut, Soraya O. Sandoval, Ryan Risgaard, Carissa L. Sirois, Chen Yang, Saniya Khullar, Marin Suzuki, Xiang Huang, Qiang Chang, Xinyu Zhao, Andre M.M. Sousa, Daifeng Wang, BOMA – a machine learning framework for comparative gene expression analysis across brains and organoids, Cell Reports Methods, 3, 100409, 2023
- Saniya Khullar, Daifeng Wang, Predicting brain-regional gene regulatory networks from multi-omics for Alzheimer’s disease phenotypes and Covid-19 severity, Human Molecular Genetics, Volume 32, Issue 11, Pages 1797–1813, 2023
- Daifeng Wang, John R. Pruett Jr., Computational approaches to address data challenges in intellectual and developmental disabilities research, Journal of Neurodevelopmental Disorders, 15, 2, 2023 (Editorial for IDDRC special issue 2022)
- Shuang Liu*, Hyejung Won*, Declan Clarke*, Nana Matoba, Saniya Khullar, Yudi Mu, Daifeng Wang#, Mark Gerstein#, Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex, Genome Medicine, 14, 133, 2022
- Chirag Gupta, Jielin Xu, Ting Jin, Saniya Khullar, Xiaoyu Liu, Sayali Alatkar, Feixiong Cheng, Daifeng Wang, Single-cell network biology characterizes cell-type gene regulation for drug repurposing and phenotype prediction in Alzheimer’s disease, PLoS Computational Biology, 18(7): e1010287, 2022
- 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 (IDDRC 2022 special issue on computational neuroscience)
- 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 (News & Views)
- 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
- Nam D Nguyen, Ian K Blaby, Daifeng Wang, ManiNetCluster: A Manifold Learning Approach to Reveal the Functional Linkages Across Multiple Gene Networks, BMC Genomics 20, 1003, 2019