Abstract
Cancer is the most fatal cause of death and determination of the reasons, making early diagnosis and correct treatment reduces the loss of lives but humans are still far away to produce a complete and permanent solutions to this problem. Nowadays, RNA and gene researches try make this solutions step by step more effective to defect cancer and to improve these researches. However, the number of the genes and complexity of the data makes analysis and experiments more challenging for humans thus, computerized solutions such as machine learning models are needed. This paper presents preliminary results of five types of tumor classification on RNA-Seq. Three machine learning models, Support Vector Machine, Backpropagation neural network and Decision Tree is implemented and various experiments are performed for this task. Obtained results show that machine learning models can effectively be used for tumor classification using gene information and Support Vector Machine achieved superior results than other considered models.
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Tuncal, K., Ozkan, C. (2020). Tumor Classification Using Gene Expression and Machine Learning Models. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham. https://doi.org/10.1007/978-3-030-35249-3_85
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DOI: https://doi.org/10.1007/978-3-030-35249-3_85
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