Cloud-based Data Mining for Cancer Drug Synergy Analysis: Applications in Non-small Cell Lung Cancer Treatment

Abstract

Cloud computing technologies have revolutionized biomedical data analysis by providing scalable infrastructure for processing large-scale cancer genomics datasets. This study presents a comprehensive framework for drug synergy analysis in non-small cell lung cancer treatment using cloud-based data mining approaches. The research integrates distributed computing architectures with machine learning algorithms to analyze multi-omics cancer data and predict optimal drug combinations. Our methodology leverages cloud storage solutions and security protocols to handle sensitive medical information while maintaining computational efficiency. The framework incorporates feature extraction techniques from genomic and transcriptomic data, combined with pharmacokinetic parameters to enhance prediction accuracy. Experimental validation using clinical NSCLC datasets demonstrates significant improvements in computational scalability and treatment recommendation precision. The cloud-based infrastructure reduces processing time by 65% compared to traditional single-machine approaches while maintaining data security standards. Results indicate enhanced drug synergy prediction capabilities with 89.3% accuracy in identifying effective combination therapies. This research contributes to precision oncology by providing clinicians with robust tools for personalized treatment selection, potentially improving patient outcomes through optimized therapeutic strategies.