ArticleBMC systems biology2018
KDiamend: a package for detecting key drivers in a molecular ecological network of disease.
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The role of bacterial infections in rheumatoid arthritis development and novel therapeutic interventions: Focus on oral infections.Journal of clinical laboratory analysis · 2023Review
- Variations in oral microbiome profiles in rheumatoid arthritis and osteoarthritis with potential biomarkers for arthritis screening.Scientific reports · 2018Article
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Authors and funding
6 authors.
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Abstract
backgroundMicrobial abundance profiles are applied widely to understand diseases from the aspect of microbial communities. By investigating the abundance associations of species or genes, we can construct molecular ecological networks (MENs). The MENs are often constructed by calculating the Pearson correlation coefficient (PCC) between genes. In this work, we also applied multimodal mutual information (MMI) to construct MENs. The members which drive the concerned MENs are referred to as key drivers.
resultsWe proposed a novel method to detect the key drivers. First, we partitioned the MEN into subnetworks. Then we identified the most pertinent subnetworks to the disease by measuring the correlation between the abundance pattern and the delegated phenotype-the variable representing the disease phenotypes. Last, for each identified subnetwork, we detected the key driver by PageRank. We developed a package named KDiamend and applied it to the gut and oral microbial data to detect key drivers for Type 2 diabetes (T2D) and Rheumatoid Arthritis (RA). We detected six T2D-relevant subnetworks and three key drivers of them are related to the carbohydrate metabolic process. In addition, we detected nine subnetworks related to RA, a disease caused by compromised immune systems. The extracted subnetworks include InterPro matches (IPRs) concerned with immunoglobulin, Sporulation, biofilm, Flaviviruses, bacteriophage, etc., while the development of biofilms is regarded as one of the drivers of persistent infections.
conclusionKDiamend is feasible to detect key drivers and offers insights to uncover the development of diseases. The package is freely available at http://www.deepomics.org/pipelines/3DCD6955FEF2E64A/ .
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