A comparative study of different classification algorithms on RNA-Seq cancer data

Main Article Content

Nihat Yilmaz Simsek
Bulent Haznedar
Cihan Kuzudisli


Gene mutations are the most important reason of cancer diseases, and there are different kind of causing genes across these diseases. RNA-Seq technology enables us to allow for gathering information about many genes simultaneously; hence, RNA-Seq data can be used for cancer diagnosis and classification. In this study, RNA-Seq dataset for renal cell cancer is analysed using three different developed classification methods: random forest (RF), artificial neural network (ANN) and deep learning (DL). The genes in our dataset are related to the following cancer types: kidney renal papillary cell, kidney renal clear cell and kidney chromophore carcinomas. It suggests that the DL method gives the highest accuracy rate compared to RF and ANN for 95.15%, 91.83% and 89.22%, respectively. We believe that the results acquired in this study will make a contribution to the classification of cancer types and support doctors in their processes of decision making.


Keywords: Classification, gene-expression, RNA-Seq, DL.


Download data is not yet available.

Article Details

How to Cite
Simsek, N. Y., Haznedar, B., & Kuzudisli, C. (2020). A comparative study of different classification algorithms on RNA-Seq cancer data. New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, (12), 24–35. https://doi.org/10.18844/gjpaas.v0i12.4983