Analysis of Microarray Data using Artificial Intelligence Based Techniques
arXiv:1507.02870 · doi:10.4018/978-1-5225-0427-6.ch011
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.
32 pages, 4 figures
References in corpus (9)
- In silico prediction of protein-protein interactions in human macrophages
- A Brief Review of Nature-Inspired Algorithms for Optimization
- Artificial Neural Networks for Beginners
- Recurrent Neural Network Based Hybrid Model of Gene Regulatory Network
- A Comprehensive Evaluation of Machine Learning Techniques for Cancer Class Prediction Based on Microarray Data
- A Novel Anticlustering Filtering Algorithm for the Prediction of Genes as a Drug Target
- Evolutionary algorithms in genetic regulatory networks model
- Formal Concept Analysis for Knowledge Discovery from Biological Data
- Ant Colony Optimization for Inferring Key Gene Interactions