Artificial intelligence in genomics

Articles

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  Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression

  Designing a supervised latent factor framework for snRNA-seq human brain, the authors find distinct Alzheimer’s-predictive gene modules across celltypes, suggesting subcelltype disease progression trajectories.

  • Liam Hodgson
  • Yue Li
  • Danilo Bzdok

  ArticleOpen Access17 May 2024 Communications Biology
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  Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging

  A study utilizing unsupervised deep learning to generate interpretable brain imaging phenotypes from brain T1 and T2-FLAIR MRI identified 97 genetic loci enhancing understanding of brain structure genetics.

  • Khush Patel
  • Ziqian Xie
  • Degui Zhi

  ArticleOpen Access5 Apr 2024 Communications Biology
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  A platform-independent AI tumor lineage and site (ATLAS) classifier

  ATLAS is a pair of AI Tumor Lineage and Site-of-origin machine learning models, that can accurately classify both primary and metastatic tumors using high-throughput RNA expression data and can identify de-differentiated anaplastic tumors.

  • Nicholas R. Rydzewski
  • Yue Shi
  • Shuang G. Zhao

  ArticleOpen Access13 Mar 2024 Communications Biology
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  Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

  Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes.

  • Zhijian Yang
  • Junhao Wen
  • Christos Davatzikos

  ArticleOpen Access8 Jan 2024 Nature Communications
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  PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants

  Here, authors present PhenoSV, a phenotype-aware machine-learning model for the functional interpretation of various types of structural variants (SVs) and genes within or outside SVs, facilitating the extraction of biological insights from coding and noncoding SVs.

  • Zhuoran Xu
  • Quan Li
  • Kai Wang

  ArticleOpen Access28 Nov 2023 Nature Communications
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  Novel Alzheimer’s disease genes and epistasis identified using machine learning GWAS platform

  • Mischa Lundberg
  • Letitia M. F. Sng
  • Natalie A. Twine

  ArticleOpen Access17 Oct 2023 Scientific Reports
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  Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease

  The authors develop deep learning models to identify genome-wide polyA sites at nucleotide resolution and calculate site strength. They further examine genomic parameters regulating site usage and reveal genetic variants altering polyA activity.

  • Emily Kunce Stroup
  • Zhe Ji

  ArticleOpen Access15 Nov 2023 Nature Communications
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  Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn’s disease patients

  • Daniele Raimondi
  • Haleh Chizari
  • Yves Moreau

  ArticleOpen Access9 Nov 2023 Scientific Reports
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  Adversarial and variational autoencoders improve metagenomic binning

  AVAMB is a deep learning ensemble approach for metagenomics binning that achieves state-of-the-art binning performance and increased taxonomic diversity on both synthetic and real datasets.

  • Pau Piera Líndez
  • Joachim Johansen
  • Simon Rasmussen

  ArticleOpen Access21 Oct 2023 Communications Biology
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  Improving genetic risk prediction across diverse population by disentangling ancestry representations

  A deep-learning framework leverages data from diverse populations and disentangles ancestry from the phenotype-relevant information in its representation.

  • Prashnna K. Gyawali
  • Yann Le Guen
  • Zihuai He

  ArticleOpen Access22 Sep 2023 Communications Biology
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  Combination of expert guidelines-based and machine learning-based approaches leads to superior accuracy of automated prediction of clinical effect of copy number variations

  • Tomáš Sládeček
  • Michaela Gažiová
  • Tomáš Szemes

  ArticleOpen Access29 Jun 2023 Scientific Reports