Our computational methods and models are available as open-source tools at the lab’s GitHub site. We regularly maintain the tools. Please contact daifeng.wang@wisc.edu for any questions. Some tools are highlighted below.
ARTEMIS predicts continuous dynamics of gene expression, cell population, and perturbation from time-series single-cell data (Bioinformatics, ISMB 2025).
COSIME for cooperative multi-view integration with scalable & interpretable explainer (Nature Machine Intelligence, 2025).
DeepGAMI for multimodal integration and imputation to improve phenotype prediction (Genome Medicine, 2023).
JAMIE for Multi-Modal Imputation and Embedding (Nature Machine Intelligence, 2023).
CMOT: Cross-Modality Optimal Transport for multimodal inference (Genome Biology, 2023).
BOMA for comparative gene expression analysis across brains and organoids (Cell Reports Methods, 2023).
MANGEM: a web app for Multimodal Analysis of Neuronal Gene expression, Electrophysiology and Morphology (Patterns, 2023).
deepManReg, A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data (Nature Computational Science, 2022).
ECMarker to identify gene expression biomarkers for early disease stages (Bioinformatics, 2021).
scGRNom for predicting cell-type disease genes and regulatory networks (Genome Medicine, 2021).
Varmole for prioritizing disease risk variants and genes (Bioinformatics, 2021).
ManiNetCluster to reveal the functional linkages across multiple gene networks (BMC Genomics, 2019).
Daifeng Wang Laboratory 