ArticleBioinformatics (Oxford, England)2020
Network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Disease-disease interactions: molecular links of neurodegenerative diseases with cancer, viral infections, and type 2 diabetes.Translational neurodegeneration · 2025Review
- Exploring biomarkers and molecular mechanisms of Type 2 diabetes mellitus promotes colorectal cancer progression based on transcriptomics.Scientific reports · 2025Article
- Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction.BMC bioinformatics · 2024Article
- A network-based trans-omics approach for predicting synergistic drug combinations.Communications medicine · 2024Article
- Network medicine: an approach to complex kidney disease phenotypes.Nature reviews. Nephrology · 2023Review
- Progress in genetics of type 2 diabetes and diabetic complications.Journal of diabetes investigation · 2023Review
- Exploring Potential Biomarkers and Molecular Mechanisms of Ischemic Cardiomyopathy and COVID-19 Comorbidity Based on Bioinformatics and Systems Biology.International journal of molecular sciences · 2023Article
- Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond.Nature communications · 2023Article
- Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs.Molecules (Basel, Switzerland) · 2023Review
- DapBCH: a disease association prediction model Based on Cross-species and Heterogeneous graph embedding.Frontiers in genetics · 2023Article
- Modular networks and genomic variation during progression from stable angina pectoris through ischemic cardiomyopathy to chronic heart failure.Molecular medicine (Cambridge, Mass.) · 2022Article
- Immune-Related Protein Interaction Network in Severe COVID-19 Patients toward the Identification of Key Proteins and Drug Repurposing.Biomolecules · 2022Article
- A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine.Briefings in bioinformatics · 2021Article
- Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery.NPJ systems biology and applications · 2021Article
- Incorporating Patient Similarity and Clinical Temporality in Disease Prognostic Modeling.IEEE transactions on computational biology and bioinformaticsArticle
Corrections and comments
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3 authors.
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Abstract
motivationDisease states are distinguished from each other in terms of differing clinical phenotypes, but characteristic molecular features are often common to various diseases. Similarities between diseases can be explained by characteristic gene expression patterns. However, most disease-disease relationships remain uncharacterized.
resultsIn this study, we proposed a novel approach for network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukaemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis and inflammatory bowel disease. We quantified disease-disease similarities based on proximities of abnormally expressed genes in various molecular networks, and showed that similarities between diseases could be explained by characteristic molecular network topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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