Convolutional neural network models for cancer treatment response prediction

Abstract EN

Recently, efforts are exerted on cancer treatment prediction based on the biomarkers related to the tumor.

gene expression and mutation profiles are the most used biomarkers for cancer prediction.

machine learning and deep learning algorithms have been used to predict drug response.

the recent research show that the performance of deep learning models is better than the performance of machine learning based one.

in this paper, convolutional neural network (CNN) models use are introduced to predict different drugs response.

deepinsight algorithm used to convert the input data to images to be more suitable as input to the CNN.

three different pretrained CNNs-models (inceptionV3, Xception, efficientNetB7) are introduced with alternatives in their settings of the training process and modification in their architectures to be able to predict the drug response using IC 50 regression values.

those models are selected due to their efficiency for ImageNet applications.

the proposed modified Xception model achieves the best accuracy over the 2 others.

at first, the whole data input passes through deepinsight which converts the gene expression data and mutation data to images.

dimension reduction is then applied using the helper technique inside the deepisignt.

comparative analysis with other deep models, shows that the proposed approach improve the prediction accuracy in a range between 14% and 22% as a reduction in mean squared error (MSE).