Abstract
Motivation: Complex diseases are due to the dense interactions of many disease-associated factors that dysregulate genes that in turn form the so-called disease modules, which have shown to be a powerful concept for understanding pathological mechanisms. There exist many disease module inference methods that rely on somewhat different assumptions, but there is still no gold standard or best-performing method. Hence, there is a need for combining these methods to generate robust disease modules. Results: We developed MODule IdentiFIER (MODifieR), an ensemble R package of nine disease module inference methods from transcriptomics networks. MODifieR uses standardized input and output allowing the possibility to combine individual modules generated from these methods into more robust disease-specific modules, contributing to a better understanding of complex diseases.
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CITATION STYLE
De Weerd, H. A., Badam, T. V. S., Martínez-Enguita, D., Åkesson, J., Muthas, D., Gustafsson, M., & Lubovac-Pilav, Z. (2020). MODifieR: An Ensemble R Package for Inference of Disease Modules from Transcriptomics Networks. Bioinformatics, 36(12), 3918–3919. https://doi.org/10.1093/bioinformatics/btaa235
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