Tumor Classification Using Gene Expression and Machine Learning Models
| Autor: | Cagri Ozkan, Kubra Tuncal |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network business.industry Computer science Decision tree 02 engineering and technology Machine learning computer.software_genre Backpropagation Task (project management) Support vector machine ComputingMethodologies_PATTERNRECOGNITION 020901 industrial engineering & automation Gene expression 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Gene |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030352486 |
| DOI: | 10.1007/978-3-030-35249-3_85 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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