Inferring Causation in Yeast Gene Association Networks With Kernel Logistic Regression

Overview

Journal Evol Bioinform Online

Publisher Sage Publications

Specialty Biology

Date 2022 Feb 17

PMID 35173404

Authors

Amira Al-Aamri

Kamal Taha

Maher Maalouf

Andrzej Kudlicki

Dirar Homouz

Affiliations

Soon will be listed here.

Abstract

Computational prediction of gene-gene associations is one of the productive directions in the study of bioinformatics. Many tools are developed to infer the relation between genes using different biological data sources. The association of a pair of genes deduced from the analysis of biological data becomes meaningful when it reflects the directionality and the type of reaction between genes. In this work, we follow another method to construct a causal gene co-expression network while identifying transcription factors in each pair of genes using microarray expression data. We adopt a machine learning technique based on a logistic regression model to tackle the sparsity of the network and to improve the quality of the prediction accuracy. The proposed system classifies each pair of genes into either connected or nonconnected class using the data of the correlation between these genes in the whole genome. The accuracy of the classification model in predicting related genes was evaluated using several data sets for the yeast regulatory network. Our system achieves high performance in terms of several statistical measures.

Citing Articles

Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification.

Al-Aamri A, Kudlicki A, Maalouf M, Taha K, Homouz D Biology (Basel). 2023; 12(4).

PMID: 37106719 PMC: 10135911. DOI: 10.3390/biology12040518.

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