ML4Gene 🧬

where we stage and how to proceed

Workshop @ Some Confs, Some Places

[Video Recording] [Conference Page] [OpenReview]


The first two decades of this century is a revolutionised epoch for the genetics and genomics science. With transformation technological developments and plummeting costs, we moved from mapping the human genome, an international endeavour that took more than a decade and cost billions of dollars, to sequencing individual genomes for a mere fraction of the cost in a few hours. Embarking on the third decade of the twenty-first century, at The Forefront of Genomics, we are now faced with the prospect of being able not only to more accurately predict disease risk and tailor existing treatments on the basis of genetic and non-genetic factors but also to potentially cure or even eliminate some diseases entirely with gene-editing technologies.

AI and ML techniques, on the other hand, have revolutionised a wide array of scientific disciplines, providing the solutions for scientific challenges that reach a stage which has never been/imagined before. Given the fact there are lots of unresolved challenges underlying Genomics and Genetics (ML4Gene) Science, we feel urgent and decide to curate a corner which enables genomics domain scientists and ML researchers to exchange insights on ``where we stage and how to advance'' in the interplay of ``ML x Gene''.

Specifically, we have recognised progress at the present stages and identified future directions to be advanced:


  • DeepMind Best Paper Award
  • Under Construction

  • DeepMind Best Presentation
  • Under Construction

  • DeepMind Travel Award
  • Under Construction


    Under Construction

    Accepted Papers

    Under Construction


    Invited Speakers

    Organizing Committee

    Hanchen Wang [Mail]
    Cambridge, Caltech

    Program Committee

    • Benjamin Chidester (CMU)
    • Romain Lopez (Stanford, GenenTech)
    • Mariano Gabitto (UW)
    • Kemal Inecik (Helmholtz Munich)
    • Gao Xin (KAUST)
    • Lior Pachter (Caltech)


    Please contact Hanchen if you have questions.
    The webpage template is by the courtesy of CVPR 2020 Workshop on Adversarial Machine Learning in Computer Vision.