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

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Nihat Yilmaz Simsek
https://orcid.org/0000-0003-0577-2766
Bulent Haznedar
https://orcid.org/0000-0003-0692-9921
Cihan Kuzudisli
https://orcid.org/0000-0003-4774-152X

Abstract

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.

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How to Cite
Simsek, N., 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
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