Artificial Intelligence in Cancer Research: Pioneering Biomarker Discovery for Pharmacological Advances

Molecular heterogeneity of cancer and dynamic nature present major challenges to effective therapy establishment, calling for the highest-level strategies towards biomarker discovery.Herein, this review addresses the paradigm-shifting effect of artificial intelligence (AI) on the discovery of biomarkers to enhance pharmacological specificity and therapeutic efficiency.By merging multiomics information (genomic, proteomic, transcriptomic) with real-world evidence and clinical records, AI-based algorithms -deep neural networks and ensemble learning models -unveil intricate patterns for anticipating tumor behaviour, drug response, and resistance mechanisms.For instance, AI has determined biomarkers such as FCN3 and CLEC1B in hepatocellular carcinoma with 93-98% diagnostic accuracy, whereas recurrent neural networks (RNNs) have mapped angiogenesis-related therapeutic targets in pancancer research.These technologies permit stratification of patient subgroups on the basis of molecular signatures to facilitate personalized treatment and drug repurposing via in silico simulations.Significant progress is AI-assisted histopathological image analysis to predict molecular phenotypes and immune checkpoint expression, reducing diagnostic variation by 30-40% in validation experiments.Besides, explainable AI (XAI) platforms boost clinical confidence by linking biomarker predictions with tumor microenvironment features such as hypoxia or immune invasion, which translate to survival measures.Nevertheless, there continues to be challenges in standardizing data, model interpretation, and ethically implementing them.Studies indicate that AI-curated biomarker sets improve detection rates in the early stages by 2% in high-mortality cancers (such as ovarian, pancreatic) and predict responses to immunotherapies with AUC-ROC of up to 0.92.Combination of AI with single-cell sequencing and spatial transcriptomics further restricts biomarker specificity to enable dynamic clonal evolution tracking under therapy.Algorithmic bias and restricted diversity in training datasets, however, risk exacerbating healthcare disparities, rendering federated learning and sound validation frameworks critical.In summary, AI-aided biomarker discovery is an oncology paradigm that repairs molecular understanding with clinical application to propel precision pharmacology.Future efforts must be aimed at scalable