Interaction-Based Aggregation of mRNA and miRNA Expression Profiles to Differentiate Myelodysplastic Syndrome
In this work we integrate conventional mRNA expression profiles with miRNA expressions using the knowledge of their validated or predicted interactions in order to improve class prediction in genetically determined diseases. The raw mRNA and miRNA expression features become enriched or replaced by new aggregated features that model the mRNA-miRNA interaction. The proposed subtractive integration method is directly motivated by the inhibition/degradation models of gene expression regulation. The method aggregates mRNA and miRNA expressions by subtracting a proportion of miRNA expression values from their respective target mRNAs. Further, its modification based on singular value decomposition that enables different subtractive weights for different miRNAs is introduced. Both the methods are used to model the outcome or development of myelodysplastic syndrome, a blood cell production disease often progressing to leukemia. The reached results demonstrate that the integration improves classification performance when dealing with mRNA and miRNA features of comparable significance. The proposed methods are available as a part of the web tool miXGENE.
KeywordsGene expression Machine learning microRNA Classification Prior knowledge Myelodysplastic syndrome
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