Year: 2020
Type: article
Abstract: Currently, most diseases are diagnosed only after significant disease-associated transformations have taken place. Here, we propose an approach able to identify when systemic qualitative changes in biological systems happen, thus opening the possibility for therapeutic interventions before the occurrence of symptoms. The proposed method exploits knowledge from biological networks and longitudinal data using a system impact analysis. The method is validated on eight biological phenomena, three synthetic datasets and five real datasets, for seven organisms. Most importantly, the method accurately detected the transition from the control stage (benign) to the early stage of hepatocellular carcinoma on an eight-stage disease dataset.
Source: Scientific Reports
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,870
Funders: National Science Foundation, U.S. Department of Health and Human Services, National Institutes of Health, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Child Health and Human Development, Division of Biological Infrastructure, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NHLBI Division of Intramural Research
Awards: G1284009618, G1348649281, G1985304581, G2671377752, G3388402394, Pathway-Guide: A novel tool for the analysis of signaling and metabolic pathways, G49035119, ABI: Novel Tools for the Analysis & Interpretation of Gene Signaling Pathways, G8649546003, Analysis of gene signaling pathways with applications in obesity and diabetes, G8887654449, G9000307077
OpenAlex ID: W3025931946