Since tumor is seriously harmful to human health, effective diagnosis measures are in urgent need for tumor therapy. Early detection of tumor is particularly important for better treatment of patients. Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep learning approach to cancer detection, and to the identification of genes critical for the diagnosis of breast cancer. First, we used PCA as a dimensionality reduction method to choose the critical genes in BRCA. Next, we trained a deep learning neural network (MLP) on the extracted genes. Lastly, we test our model on other samples that were not included in our training data to illustrating that MLP are capable of classifying cancer samples from gene expression data with an accuracy of %98.
taylankabbani/CLASSIFICATION-OF-TUMOR-GENE-EXPRESSION-USING-ARTIFICAL-NEURAL-NETWORKS
CLASSIFICATION OF TUMOR GENE EXPRESSION USING ARTIFICAL NEURAL NETWORKS
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