Biomarker identification and cancer survival prediction using random spatial local best cat swarm and Bayesian optimized DNN

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.& COPY; 2023 Elsevier B.V. All rights reserved.

Automated summary

This research proposes a framework called BioSurv for identifying cancer biomarkers and predicting cancer survival using machine learning and deep learning techniques. Multi-omics data from breast cancer and lung adenocarcinoma are analyzed, and statistical tests and an optimization algorithm are employed for feature selection. Thirteen BRCA and fifteen LUAD poor prognostic markers are identified, and a Bayesian optimized deep neural network achieves high accuracy in cancer survival prediction for both types of cancer.