Innovative Machine Learning Framework for Assessing Drug Response in Cancer Treatment

The lifespan and cancer therapy have improved due to bioinformatics advances. Mutations and changes in a patient’s cell genes create cancer, which is tough to treat, particularly in the tumor microenvironment. More tailored treatments are needed since even people with the same cancer respond differently to similar medicines. Researchers seek faster alternatives to clinical trials for medication discovery. Big data on oncology and pharmacogenomics has boosted the sector. Machine learning and data mining play crucial roles in interpreting this knowledge and driving cancer therapy advancements. This work proposes a kernelized similarity-based periodicity matrix decomposition methodology for predicting cancer-fighting treatment response, assuming comparable pharmacological responses. Critical characteristics for the goal function are drug-drug chemical similarities in structure and tissue gene expression. Maps non-linear drugtissue connections using the kernel function. We present an integrated methodology for finding synergistic cancer prevention medication combinations. Features from individual drug response data are used to predict drug interaction in our integrated strategy. On those extracted characteristics, proposed KSRMF model outperformed compared to all other machine learning models by having accuracy of 9 2. 4 5% and highest performance. We evaluated our approach on mutantBRAF carcinoma and AstraZeneca-Sanger Drug Combining Information Prediction DREAM Challenge cell lines. This system for medication synergy prediction is strong and effectiveness, promising individualized cancer therapy.