ArticlePloS one2020
Unveiling new disease, pathway, and gene associations via multi-scale neural network.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review.Frontiers in medicine · 2023Pooled it
- Sparsity is all you need: rethinking biologically informed neural networks.Briefings in bioinformatics · 2026Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Exploring novel disease-disease associations based on multi-view fusion network.Computational and structural biotechnology journal · 2023Article
- Network analysis reveals rare disease signatures across multiple levels of biological organization.Nature communications · 2021Article
- Classification and Functional Analysis between Cancer and Normal Tissues Using Explainable Pathway Deep Learning through RNA-Sequencing Gene Expression.International journal of molecular sciences · 2021Article
- Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data.BMC bioinformatics · 2021Article
- Mapping the multiscale structure of biological systems.Cell systems · 2021Review
- ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network.PLoS computational biology · 2021Article
- Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder.Nature communications · 2020Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diseases involve complex modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, new biological knowledge about a disease can be extracted from these profiles, improving our ability to diagnose and assess disease risks. This knowledge can be used for drug re-purposing, or by physicians to evaluate a patient's condition and co-morbidity risk. Here, we consider differential gene expressions obtained by microarray technology for patients diagnosed with various diseases. Based on these data and cellular multi-scale organization, we aim at uncovering disease-disease, disease-gene and disease-pathway associations. We propose a neural network with structure based on the multi-scale organization of proteins in a cell into biological pathways. We show that this model is able to correctly predict the diagnosis for the majority of patients. Through the analysis of the trained model, we predict disease-disease, disease-pathway, and disease-gene associations and validate the predictions by comparisons to known interactions and literature search, proposing putative explanations for the predictions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.