Abstract
The analysis of gene expression data is a complex task, and many tools and pipelines are available to handle big sequencing datasets for case-control (bivariate) studies. In some cases, such as pilot or exploratory studies, the researcher needs to compare more than two groups of samples consisting of a few replicates. Both standard statistical bioinformatic pipelines and innovative deep learning models are unsuitable for extracting interpretable patterns and information from such datasets. In this work, we apply a combination of fuzzy rule systems and genetic algorithms to analyze a dataset composed of 21 samples and 6 classes, useful for approaching the study of expression profiles in ovarian cancer, compared to other ovarian diseases. The proposed method is capable of performing a feature selection among genes that is guided by the genetic algorithm, and of building a set of if-then rules that explain how classes can be distinguished by observing changes in the expression of selected genes. After testing several parameters, the final model consists of 10 genes involved in the molecular pathways of cancer and 10 rules that correctly classify all samples.
Highlights
Among the most common cancers in women, ovarian cancer is the most lethal, due to its late symptoms and diagnosis, and its onset can be a primary tumor or secondary tumor of the fallopian tube or endometrium [1]
The analysis aims to extract information on changes in gene expression that can be useful for discriminating between different tissues, and to study the molecular mechanisms that differ in the samples
They are written by using terms coming from natural language, such as the names of the genes, the terms medium, under, and overexpression, that are commonly used by the domain experts, and the derived classes refer to different diseases, as classified by experts
Summary
Among the most common cancers in women, ovarian cancer is the most lethal, due to its late symptoms and diagnosis, and its onset can be a primary tumor or secondary tumor of the fallopian tube or endometrium [1]. NGS experiments are quite expensive and require further laboratory validation of the most significant results, as they can present noise in the data that stems from the inherent complexity of the technology This is why many researchers use NGS with a limited number of samples to extract the most evident molecular activities and validate those results only on a larger number of samples. NGS results are highly dependent on the laboratory experimental settings used and the datasets produced with different technical conditions (sequencer type, tissue type, tissue conservation, etc.) are not directly comparable. This is why NGS data are mainly exploited for case-control studies with only two conditions
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