Journal
PLOS ONE
Volume 15, Issue 4, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0231059
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Funding
- European Research Council (ERC) Consolidator Grant [770827]
- UCL Computer Science
- Slovenian Research Agency project [J1-8155]
- Serbian Ministry of Education and Science Project [III44006]
- Prostate Project
- Fondation Toulouse Cancer Sante and Pierre Fabre Research Institute as part of the Chair of Bio-Informatics in Oncology of the CRCT [BES-2016077403]
- Spanish Ministry of Economics and Competitiveness [BFU2015-71241-R]
- European Research Council (ERC) [770827] Funding Source: European Research Council (ERC)
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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 diseasedisease, 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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