Abstract
Machine learning models have become increasingly important in predicting gene expression profiles from various biological datasets. These models can help understand gene regulatory mechanisms and predict gene activity under different conditions, such as disease states or drug treatments. This article reviews the use of machine learning techniques, including supervised learning, deep learning, and ensemble models, for predicting gene expression profiles. We explore how these models integrate multi-omics data, their applications in genomics and biomedical research, and discuss challenges and future directions in gene expression prediction
All articles published in the American Journal of Bioinformatics (AJB) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license, authors retain full copyright of their work and grant permission to anyone to:
-
Share — copy and redistribute the material in any medium or format
-
Adapt — remix, transform, and build upon the material for any purpose, even commercially
Conditions:
-
Attribution: Appropriate credit must be given to the original author(s) and the source, a link to the license must be provided, and any changes made must be indicated.
-
No additional restrictions: You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
This open-access license ensures that scholarly work published in AJB is freely accessible and usable, promoting knowledge dissemination and academic collaboration worldw