A gene co-expression network (CEN) is of biological interest, since co-expressed genes share commonfunctions and biological processes or pathways. Finding relationships among modules can reveal inter-modularpreservation, and similarity in transcriptome, functional, and biological behaviors among modules of the sameor two different datasets. There is no method which explores the one-to-one relationships and one-to-manyrelationships among modules extracted from control and disease samples based on both topological andsemantic similarity using both microarray and RNA seq data. In this work, we propose a novel fusion measureto detect mapping between modules from two sets of co-expressed modules extracted from control and diseasestages of Alzheimer’s disease (AD) and Parkinson’s disease (PD) datasets. Our measure considers bothtopological and biological information of a module and is an estimation of four parameters, namely, semanticsimilarity, eigengene correlation, degree difference, and the number of common genes. We analyze the consensusmodules shared between both control and disease stages in terms of their association with diseases. Wealso validate the close associations between human and chimpanzee modules and compare with the state-ofthe-art method. Additionally, we propose two novel observations on the relationships between modules forfurther analysis.
Volume 46, 2021
Continuous Article Publishing mode
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