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Welcome to Daifeng Wang Laboratory!

  • ARTEMIS integrates autoencoders and schrödinger bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series single-cell data, Bioinformatics (ISMB/ECCB 2025), 41, supplement 1, 189-197, 2025
  • BOMA – a machine learning framework for comparative gene expression analysis across brains and organoids, Cell Reports Methods, 3, 100409, 2023
  • CMOT: Cross-Modality Optimal Transport for multimodal inference, Genome Biology, 24, 163, 2023
  • COSIME: Cooperative multi-view integration with Scalable and Interpretable Model Explainer, Nature Machine Intelligence, 7, 1636–1656, 2025
  • CoTF-reg reveals cooperative transcription factors in oligodendrocyte gene regulation using single-cell multi-omics, Communications Biology, 8, 181, 2025
  • DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve phenotype prediction, Genome Medicine, 15, 88, 2023
  • A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data, Nature Computational Science, 2, 38–46, 2022
  • 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
  • Joint Variational Autoencoders for Multi-Modal Imputation and Embedding, Nature Machine Intelligence, 5, 631–642, 2023
  • Manifold learning analysis suggests strategies for aligning single-cell multi-modalities and revealing functional genomics for neuronal electrophysiology, Communications Biology, 4, 1308, 2021
  • ManiNetCluster: A Manifold Learning Approach to Reveal the Functional Linkages Across Multiple Gene Networks, BMC Genomics 20, 1003, 2019
  • Network-based drug repurposing for psychiatric disorders using single-cell genomics, Cell Genomics, 101003, 2025
  • scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks, Genome Medicine, 13, 95, 2021
  • 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
  • Varmole: A biologically drop-connect deep neural network model for prioritizing disease risk variants and genes, Bioinformatics, 37 (12), 1772-1775, 2021
  • MANGEM: a web app for Multimodal Analysis of Neuronal Gene expression, Electrophysiology and Morphology, Patterns, 4, 100847, 2023
  • NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation, Bioinformatics Advances, vbae206, 2025
  • 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
  • Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases, Journal of Neurodevelopmental Disorders, 14, 28, 2022
  • Protocol for comparative gene expression data analysis between brains and organoids using a cloud-based web-app, STAR Protocols, 5, 103375, 2024
  • Multi-view learning for understanding functional multiomics, PLoS Computational Biology, 16(4): e1007677, 2020
  • Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages, BMC Cancer, 17:830, 2017

We develop machine learning and artificial intelligence (ML/AI) approaches and bioinformatics tools to bridge computation and biology for mechanistic insights into complex brains and brain diseases. Our applications focus on functional genomics, gene regulation, cell dynamics and neural circuits. We are grateful for the funding support from National Institutes of Health, National Science Foundation, Simons Foundation Autism Research Initiative, and the University of Wisconsin-Madison. Please see our Research, Publications, Tools, and News!