COMPUTATIONAL APPROACHES TO ANALYZING GENETIC DATA: IDENTIFICATION OF BIOMARKERS FOR CANCER DETECTION

Abstract

This study explores the application of computational approaches to analyze genetic data for the identification of potential biomarkers for cancer detection. Leveraging large-scale cancer datasets from public repositories, including gene expression profiles, mutation data, and proteomics information, we applied a series of machine learning and deep learning models to identify key biomarkers. Our analysis revealed that convolutional neural networks (CNNs) significantly outperformed traditional machine learning models, such as support vector machines (SVM) and random forests (RF), achieving higher accuracy (92%), sensitivity (89%), specificity (91%), and area under the curve (AUC) (0.94). The inclusion of genome and transcriptomic and proteomic data with multi-omics information helped boost model performance as it delivered an extensive biomarker identification framework. Multiple data omic tests generated predictive results superior to solitary data omic approaches which demonstrates why researchers need multiple integrated datasets in cancer studies. These findings establish deep learning and integrated system methods as effective tools for identifying fresh biomarkers that connect to various cancer types based on current research results. While the computational results indicate usefulness the authors highlight that extensive clinical validation of newly discovered biomarkers must happen alongside enhanced computational methodology refinement to achieve reliable and generalized results. High-end computational processes create new opportunities in cancer detection and personalized medicine while demonstrating their potential for diagnosis transformation through this research.