ArticleNPJ systems biology and applications2019
A data driven approach reveals disease similarity on a molecular level.
Article in NPJ systems biology and applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- XPF interacts with TOP2B for R-loop processing and DNA looping on actively transcribed genes.Science advances · 2023Article
- Towards Whole Health Toxicology: In-Silico Prediction of Diseases Sensitive to Multi-Chemical Exposures.Toxics · 2022Article
- The Essentials of Multiomics.The oncologist · 2022Article
- Human microRNA similarity in breast cancer.Bioscience reports · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Could there be unexpected similarities between different studies, diseases, or treatments, on a molecular level due to common biological mechanisms involved? To answer this question, we develop a method for computing similarities between empirical, statistical distributions of high-dimensional, low-sample datasets, and apply it on hundreds of -omics studies. The similarities lead to dataset-to-dataset networks visualizing the landscape of a large portion of biological data. Potentially interesting similarities connecting studies of different diseases are assembled in a disease-to-disease network. Exploring it, we discover numerous non-trivial connections between Alzheimer's disease and schizophrenia, asthma and psoriasis, or liver cancer and obesity, to name a few. We then present a method that identifies the molecular quantities and pathways that contribute the most to the identified similarities and could point to novel drug targets or provide biological insights. The proposed method acts as a "statistical telescope" providing a global view of the constellation of biological data; readers can peek through it at: http://datascope.csd.uoc.gr:25000/.
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Registered trials
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