DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.

Journal: Genome medicine
Published Date: Oct 31, 2023

Abstract

BACKGROUND: Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models.

Authors

  • Pramod Bharadwaj Chandrashekar
    Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
  • Sayali Alatkar
    Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
  • Jiebiao Wang
    Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
  • Gabriel E Hoffman
    Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Chenfeng He
    Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
  • Ting Jin
    Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA.
  • Saniya Khullar
    Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
  • Jaroslav Bendl
    Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • John F Fullard
    Center for Disease Neurogenomics, Department of Psychiatry and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
  • Panos Roussos
    Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Daifeng Wang

Keywords

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