Classification des cancers basée sur la sélection des gènes des données biopuces

Abstract

This thesis aims to address a major challenge in cancer research, namely the identification of the most relevant genes for cancer classification. To achieve this, a three-step approach was adopted. Firstly, classification algorithms were applied directly to biochip datasets. Subsequently, data quality was improved by applying preprocessing steps before reapplying the classification algorithms. Finally, preprocessed data was further enhanced by selecting the most relevant genes using selection techniques based on mutual information filtering, before reapplying the same classification algorithms. The results of this study revealed that the support vector machine algorithm achieved a classification rate of 100% with most of the databases used after selecting the relevant genes. The neural network algorithm also showed good performance in classifying cancer types.