ArticleNPJ systems biology and applications2021
Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits.
Article in NPJ systems biology and applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A machine learning approach to assess Sustainable Development Goals food performances: The Italian case.PloS one · 2024Article
- Article
- Gene communities in co-expression networks across different tissues.PLoS computational biology · 2023Article
- MTM: a multi-task learning framework to predict individualized tissue gene expression profiles.Bioinformatics (Oxford, England) · 2023Article
- Transcriptome analysis based on machine learning reveals a role for autoinflammatory genes of chronic nonbacterial osteomyelitis (CNO).Scientific reports · 2023Article
- Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets.BMC bioinformatics · 2022Article
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4 authors.
Funding
Abstract
Here, we performed a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome. We generated an atlas of communities in gene co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (representing nearly 18% of all genes) robustly identified biologically-meaningful clusters. Notably, new gene expression data can be embedded into our algorithmically derived models to accelerate discoveries in high-dimensional molecular datasets and downstream diagnostic or prognostic applications. We demonstrate the generalisability of our approach through systematic testing in external genomic and transcriptomic datasets. Methodologically, prioritisation of the communities in a transcriptome-wide association study of the biomarker C-reactive protein (CRP) in 361,194 individuals in the UK Biobank identified genetically-determined expression changes associated with CRP and led to considerably improved performance. Furthermore, a deep learning framework applied to the communities in nearly 11,000 tumors profiled by The Cancer Genome Atlas across 33 different cancer types learned biologically-meaningful latent spaces, representing metastasis (p < 2.2 × 10
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