Analysis of Microarray Data using Artificial Intelligence Based Techniques
This is an incremental review paper for biologists and data analysts, summarizing existing methods without introducing novel solutions.
The paper reviews artificial intelligence techniques, such as neural networks and genetic algorithms, for analyzing microarray gene expression data to improve insights into biological processes, but it does not present new results or concrete numbers.
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.