Predicting the S. cerevisiae Gene Expression Score by a Machine Learning Classifier

TL;DR

This paper uses machine learning to predict gene expression scores in yeast based on various attributes.

Contribution

A novel random forest model is developed to identify key attributes influencing gene expression scores in Saccharomyces cerevisiae.

Findings

The random forest model achieved 84.1% accuracy in classifying gene expression scores.

Key attributes include experimental conditions and genetic, physical, statistical, and logistic features.

The model distinguishes low, moderate, and high expression score classes effectively.

Abstract

The topic of this work is gene expression and its score according to various factors analyzed globally using machine learning techniques. The expression score (ES) of genes characterizes their activity and, thus, their importance for cellular processes. This may depend on many different factors (attributes). To find the most important classifier, a machine learning classifier (random forest) was selected, trained, and optimized on the Waikato Environment for Knowledge Analysis WEKA platform, resulting in the most accurate attribute-dependent prediction of the ES of Saccharomyces cerevisiae genes. In this way, data from the Saccharomyces Genome Database (SGD), presenting ES values corresponding to a wide spectrum of attributes, were used, revised, classified, and balanced, and the significance of the considered attributes was evaluated. In this way, the novel random forest model indicates the most important attributes determining classes of low, moderate, and high ES. They cover both the experimental conditions and the genetic, physical, statistical, and logistic features. During validation, the obtained model could classify the instances of a primary unknown test set with a correctness of 84.1%.

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