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A Survey on Deep Learning Techniques for Prognosis and Diagnosis of Cancer from Microarray Gene Expression Data

The gene expression classification and identification from DNA microarray data is efficient technique for cancer diagnosis and prognosis for specific cancer subtypes. DNA microarray technology has great potential to discover information from expression levels of thousands of gene. The
 collection of significant genes which can improve the accuracy can give proper direction in early diagnosis of cancer. Cancer may be of different subtypes. Cancer detection from microarray gene expression data has major challenge of low sample size, high dimensionality and complexity of the
 data. There is a need for fast and computationally efficient method to deal with these kind of challenges. Deep Learning has succeeded in numerous fields such as image, video, speech, and text processing. Gene expression analysis is a unique challenge to Deep Learning for various cancer detection
 and prediction tasks in order to set specific biomarkers for different cancer subtypes. In this paper, we briefly discuss the strengths of different Deep Learning architectures for a cancer detection and prediction of various types of cancer through gene expression analysis.