特征选择
计算机科学
人工智能
鉴定(生物学)
生物标志物
随机森林
癌症
机器学习
模式识别(心理学)
内科学
生物
医学
生物化学
植物
作者
Arwinder Dhillon,Ashima Singh,Vinod K. Bhalla
标识
DOI:10.1016/j.asoc.2023.110649
摘要
Identifying cancer biomarkers is crucial for improving patient outcomes and reducing cancer-related deaths. This research proposes BioSurv, a framework for biomarker identification and cancer survival prediction, using machine learning and deep learning techniques. Multi-omics data from breast cancer (BRCA) and lung adenocarcinoma (LUAD), including mRNA, miRNA, CNV, and DNA methylation, are analyzed. The collected dataset is passed to statistical tests and the random spatial local best cat swarm optimization (RSLBCSO) algorithm for feature selection, followed by KEGG and survival analyses to identify prognostic markers. Thirteen BRCA and fifteen LUAD poor prognostic markers are identified. A Bayesian optimized deep neural network (DNN) is used for cancer survival prediction, achieving high accuracy of 90% and 91% for BRCA and LUAD, respectively.
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