A Research on Cancer Subtype Classification Using Gene Expression Data

P. Avila Clemenshia and Dr.B. Mukunthan

Classifying cancer subtypes appropriately is critical for selecting the relevant treatment strategy. Detection of cancer and classification for diagnostic and prognostic determinations is generally done based on tissue sections under pathological analysis, resulting in specific data interpretations. Most of the research that are recently undergone include the determination of accurate identification of cancer subtypes, in order to identify genes which force to create cancer. In bioinformatics, microarray based cancer classification is emerging as an essential topic of research, to produce therapies and also cancer subtypes can be detected using this method. This research work gives information about the details studied on various methods of Cancer Subtypes Classification System. Recently, deep learning methodology has been proposed. Also the deep learning method will be suitable for data with high dimension and small size of sample. Since the deep learning based Cancer Subtypes Classification systems successfully classify the samples, little research has evaluated how well this system is suitable for subtype prediction in three cancer types RNA-Seq gene expression datasets, which includes, LUNG-lung cancer, GBM-glioblastoma multiform, and BRCA-breast invasive carcinoma, and are downloaded from The Cancer Genome Atlas (TCGA).

Volume 12 | 04-Special Issue

Pages: 490-500

DOI: 10.5373/JARDCS/V12SP4/20201514