ArticleOncotarget2017
Using molecular functional networks to manifest connections between obesity and obesity-related diseases.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Molecular characterization of colorectal adenoma and colorectal cancer via integrated genomic transcriptomic analysis.Frontiers in oncology · 2023Article
- Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks.Frontiers in genetics · 2021Article
- Longitudinal plasma inflammatory proteome profiling during pregnancy in the Born into Life study.Scientific reports · 2020Article
- Revealing the Interactions Between Diabetes, Diabetes-Related Diseases, and Cancers Based on the Network Connectivity of Their Related Genes.Frontiers in genetics · 2020Article
- Molecular Network-Based Identification of Competing Endogenous RNAs in Thyroid Carcinoma.Genes · 2018Article
- Identification of human age-associated gene co-expressions in functional modules using liquid association.Oncotarget · 2018Article
Corrections and comments
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Authors and funding
14 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Obesity is a primary risk factor for many diseases such as certain cancers. In this study, we have developed three algorithms including a random-walk based method OBNet, a shortest-path based method OBsp and a direct-overlap method OBoverlap, to reveal obesity-disease connections at protein-interaction subnetworks corresponding to thousands of biological functions and pathways. Through literature mining, we also curated an obesity-associated disease list, by which we compared the methods. As a result, OBNet outperforms other two methods. OBNet can predict whether a disease is obesity-related based on its associated genes. Meanwhile, OBNet identifies extensive connections between obesity genes and genes associated with a few diseases at various functional modules and pathways. Using breast cancer and Type 2 diabetes as two examples, OBNet identifies meaningful genes that may play key roles in connecting obesity and the two diseases. For example,
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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.