Simplifying p-value calculation for the unbiased microRNAenrichment analysis, using ML-techniques
EDBT/ICDT Workshops 2021
2021
Conference/Workshop
- Contact persons: Konstantinos Zagganas , Thanasis Vergoulis , Theodore Dalamagas
Abstract.
The investigation of the role of small bio-molecules (called microRNAs) in biological functions is a very popular topic in bioinformatics, since microRNAs have been shown to present novel
therapeutic methods for diseases like cancer or Hepatitis C. In
order to predict the involvement of microRNAs in biological functions many statistical approaches have been used that involve
p-value calculations, with the most popular one being Fisher’s
exact test. However, it has been shown that data distribution does
not match with any of the theoretical distributions used by the
aforementioned approaches. Thus, an empirical randomization
approach is preferred. Nevertheless, such analyses are computationally intensive. In this paper, we present a novel approach for
microRNA enrichment analysis using Machine Learning techniques, in order to predict p-values instead of calculating them
using randomization experiments. This simplifies the work for
bioinformatics data analysts, helping them to efficiently perform
multiple enrichment analysis tasks.