Abstract
BackgroundMultidimensional data mining from an integrated environment of different data sources is frequently performed in computational system biology. The molecular mechanism from the analysis of a complex network of gene-miRNA can aid to diagnosis and treatment of associated diseases.MethodsIn this work, we mainly focus on finding inflammatory bowel disease (IBD) associated microRNAs (miRNAs) by biclustering the miRNA-target interactions aided by known IBD risk genes and their associated miRNAs collected from several sources. We rank different miRNAs by attributing to the dataset size and connectivity of IBD associated genes in the miRNA regulatory modules from biclusters. We search the association of some top-ranking miRNAs to IBD related diseases. We also search the network of discovered miRNAs to different diseases and evaluate the similarity of those diseases to IBD.ResultsAccording to different literature, our results show the significance of top-ranking miRNA to IBD or related diseases. The ratio analysis supports our ranking method where the top 20 miRNA has approximately tenfold attachment to IBD genes. From disease-associated miRNA network analysis we found that 71% of different diseases attached to those miRNAs show more than 0.75 similarity scores to IBD.ConclusionWe successfully identify some miRNAs related to IBD where the scoring formula and disease-associated network analysis show the significance of our method. This method can be a promising approach for isolating miRNAs for similar types of diseases.
Highlights
MethodsWe mainly focus on finding inflammatory bowel disease (IBD) associated microRNAs (miRNAs) by biclustering the miRNA-target interactions aided by known IBD risk genes and their associated miRNAs collected from several sources
Multidimensional data mining from an integrated environment of different data sources is frequently performed in computational system biology
inflammatory bowel disease (IBD) gene set We previously proposed a method for predicting IBD risk genes based on currently known IBD risk genes collected from DisGeNet database and differentially expressed genes determined using gene expression data [7]
Summary
IBD gene set We previously proposed a method for predicting IBD risk genes based on currently known IBD risk genes collected from DisGeNet database and differentially expressed genes determined using gene expression data [7]. As mentioned above we selected 2245 IBD genes from different databases and studies For each bicluster, these genes were matched and corresponding miRNAs were separated. Relevance Score Calculation We generated IBD related sub-MRMs from 4 different MTIs (as mentioned in Table 1) separately. Ranking of the miRNAs Based on miRWalk dataset, we generated 1579 biclusters from which we found 1011 sub-MRMs encompassing 50 miRNAs and 333 genes. 64 biclusters were generated from mirTarbase dataset out of which we found 41 IBD related sub-MRMs encompassing 100 miRNAs and 128 genes. 23 biclusters were generated from small dataset miRecords where 20 sub-MRMs with 48 miRNAs and 54 genes were found. It can be concluded that our approach is a promising method for prioritizing IBD related miRNAs and this method can be applied to other diseases
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