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Selected Publications ∣ machine learning

  • Chen V, Yang M, Cui W, Kim JS, Talwalkar A*, and Ma J*.
    Applying interpretable machine learning in computational biology - pitfalls, recommendations and opportunities for new developments.
    Nature Methods, in press
  • Deng Y, Zhang R, Xu P, Ma J*, and Gu Q*.
    PhyGCN: Pre-trained gypergraph convolutional neural networks with self-supervised learning.
    Transactions on Machine Learning Research, in press
  • Chidester B, Zhou T, Alam S, and Ma J.
    SPICEMIX enables integrative single-cell spatial modeling of cell identity.
    Nature Genetics, 55(1):78-88, 2023. [Cover Article]
    RECOMB, 2021.
  • Yang Y, Wang Y, Zhang Y, and Ma J.
    CONCERT: Genome-wide prediction of sequence elements that modulate DNA replication timing.
    bioRxiv, doi:
    RECOMB, 2022.
  • Zhang R, Zhou T, and Ma J.
    Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi.
    Cell Systems, 13(10):P798-807.E6, 2022. [Cover Article]
    RECOMB, 2022.
  • Zhang R, Zhou T, and Ma J.
    Multiscale and integrative single-cell Hi-C analysis with Higashi.
    Nature Biotechnology,, 2021.
  • Zhang R, Ma JZ*, and Ma J*.
    DANGO: Predicting higher-order genetic interactions.
    bioRxiv, doi:
    RECOMB, 2021.
  • Zhang R, Zou Y, and Ma J.
    Hyper-SAGNN: a self-attention based graph neural network for hypergraphs.
    ICLR, 2020.
  • Chidester B, Zhou T, Do MN, and Ma J.
    Rotation equivariant and invariant neural networks for microscopy image analysis.
    ISMB, 2019.
    Bioinformatics, 35(14):i530-i537, 2019.
  • Yang Y, Zhang Y, Ren B, Dixon J, and Ma J.
    Comparing 3D genome organization in multiple species using Phylo-HMRF.
    Cell Systems, 8(6):494-505.e14, 2019.
    RECOMB, 2019.
  • Yang Y, Gu Q, Zhang Y, Sasaki T, Crivello J, O'Neill R, Gilbert DM, and Ma J.
    Continuous-trait probabilistic model for comparing multi-species functional genomic data.
    Cell Systems, 7(2):208-218.e11, 2018.
    RECOMB, 2018.
  • Singh S, Poczos B, and Ma J.
    Minimax reconstruction risk of convolutional sparse dictionary learning.
    AISTATS, 2018.
  • Chen J, Xu P, Wang L, Ma J, and Gu Q.
    Covariate adjusted precision matrix estimation via nonconvex optimization.
    ICML, 2018.
  • Xu P, Ma J, and Gu Q.
    Speeding up latent variable Gaussian graphical model estimation via nonconvex optimizations.
    NIPS, 2017.
  • Tian D, Gu Q*, and Ma J*.
    Identifying gene regulatory network rewiring using latent differential graphical models.
    Nucleic Acids Research, 44(17):e140, 2016.