E3S Web Conf.
Volume 185, 20202020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020)
|Number of page(s)||5|
|Section||Chemical Engineering and Food Biotechnology|
|Published online||01 September 2020|
Breast Cancer Biomarker Prediction Model Based on Principal Component Extraction and Deep Convolutional Network Integration Learning
Sichuan Agricultural University, No. 46, Xinkang Road, Ya'an, Sichuan, China
* Corresponding author: mailto:email@example.com
Effective extraction of characteristic information from sequencing data of cancer patients is an essential application for cancer research. Several prognostic classification models for breast cancer sequencing data have been established to assist patients in their treatment. However, these models still have problems such as poor robustness and low precision. Based on the convolutional network model in deep learning, we construct a new classifier PCA-1D LeNet-Ada (PLA) by using principal component extraction method, Le-Net convolution network, and Adaptive Boosting method. PLA predicts three biomarkers for breast cancer patients based on their somatic cell copy number variations and gene expression profiles.
© The Authors, published by EDP Sciences, 2020
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