Deep learning, probabilistic models, and generative methods for multi-omics, regulatory genomics, and clinical genomics.
The lab develops AI methods tailored to the structure of biomedical data, where datasets are heterogeneous, noisy, and biologically constrained. We work across deep-learning, probabilistic, and emerging generative approaches, with applications in regulatory genomics, single-cell trajectory inference, long-read variant interpretation, and translational genomics.
Foundational work includes deep-learning imputation for transcription factor binding (Qin & Feng, 2017), integrative regulatory modeling with Lisa (Qin et al., 2020), and statistical integration methods for high-dimensional, multi-source omics data (Zang et al., 2016). We also contribute to community benchmarking of open problems in single-cell analysis (Luecken et al., 2025) and to reviews on inferring developmental trajectory and cell fate (Wang et al., 2021), ensuring our methods are evaluated against the strongest baselines.
References
2025
Nat Biotechnol
Defining and benchmarking open problems in single-cell analysis
Malte D Luecken, Scott Gigante, Daniel B Burkhardt, and 14 more authors
@article{Luecken2025OpenProblems,title={Defining and benchmarking open problems in single-cell analysis},author={Luecken, Malte D and Gigante, Scott and Burkhardt, Daniel B and Cannoodt, Robrecht and Strobl, Daniel C and Markov, Nikolay S and Zappia, Luke and Palla, Giovanni and Lewis, Wesley and Dimitrov, Daniel and Vinyard, Michael E and Magruder, D S and Mueller, Michaela F and Andersson, Alma and Dann, Emma and Qin, Qian and others},journal={Nature Biotechnology},volume={43},number={7},pages={1035--1040},month=jul,year={2025},doi={10.1038/s41587-025-02694-w},}
2021
Curr Opin Syst Biol
Current progress and potential opportunities to infer single-cell developmental trajectory and cell fate
@article{Wang2021TrajectoryReview,title={Current progress and potential opportunities to infer single-cell developmental trajectory and cell fate},author={Wang, L and Zhang, Q and Qin, Qian and others},journal={Current Opinion in Systems Biology},volume={26},year={2021},doi={10.1016/j.coisb.2021.03.006}}
2020
Genome Biol
Lisa: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data
@article{Qin2020Lisa,title={{Lisa}: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and {ChIP-seq} data},author={Qin, Qian and Fan, J and Zheng, R and Wan, C and Mei, S and Wu, Q and Sun, H and Brown, M and Zhang, J and Meyer, C A and Liu, X S},journal={Genome Biology},volume={21},number={1},pages={32},year={2020},}
2017
PLoS Comput Biol
Imputation for transcription factor binding predictions based on deep learning
@article{Qin2017DeepLearningTF,title={Imputation for transcription factor binding predictions based on deep learning},author={Qin, Qian and Feng, Jianfeng},journal={PLoS Computational Biology},volume={13},number={2},pages={e1005403},year={2017},doi={10.1371/journal.pcbi.1005403},}
2016
Nat Commun
High-dimensional genomic data bias correction and data integration using MANCIE
@article{Zang2016MANCIE,title={High-dimensional genomic data bias correction and data integration using {MANCIE}},author={Zang, C and Wang, T and Deng, K and Li, B and Hu, S and Qin, Qian and Xiao, T and Zhang, S and Meyer, C A and He, H H and Brown, M and Liu, J S and Xie, Y and Liu, X S},journal={Nature Communications},volume={7},pages={11305},year={2016},}