Analysis of Microarray Data using Artificial Intelligence Based Techniques

TL;DR

This paper reviews artificial intelligence techniques like neural networks, fuzzy logic, and genetic algorithms for analyzing microarray gene expression data, highlighting challenges and future research directions in the field.

Contribution

It provides a comprehensive review of AI-based methods applied to microarray data analysis, emphasizing recent advances and future challenges.

Findings

AI techniques improve microarray data analysis accuracy

Challenges include data complexity and high dimensionality

Future work involves integrating AI methods for better insights

Abstract

Microarray is one of the essential technologies used by the biologist to measure genome-wide expression levels of genes in a particular organism under some particular conditions or stimuli. As microarrays technologies have become more prevalent, the challenges of analyzing these data for getting better insight about biological processes have essentially increased. Due to availability of artificial intelligence based sophisticated computational techniques, such as artificial neural networks, fuzzy logic, genetic algorithms, and many other nature-inspired algorithms, it is possible to analyse microarray gene expression data in more better way. Here, we reviewed artificial intelligence based techniques for the analysis of microarray gene expression data. Further, challenges in the field and future work direction have also been suggested.