Cervical cancer remains a major and widespread global health problem and continues to be a global health problem despite significant progress. One of the biggest challenges facing the healthcare system is advances in diagnosis and treatment. In this study, we combined multiple types of biological data, specifically genomic, transcriptomic and proteomic data, to find usable cancer targets and treatments that are not only prevalent but also have the potential to impact cancer progression. We provide a rich and carefully curated list of drug names and molecular targets for drug discovery using advanced techniques such as machine learning, additive pathway analysis and virtual machine analysis. This novel approach highlights the valuable role that inclusive processes can play in shaping good practice and the design of cancer research, ultimately driving studies to increase the benefits for cancer patients. For more detailed access to the GEO GSE9750 dataset used in the article, DOI: 10.1093/nar/gkm679 is available. , virtual representation.
