Authors: A. Mohamed Sikkander, Joel J. P. C. Rodrigues, Manoharan Meena
Abstract: Artificial Intelligence (AI), also referred to as machine learning (ML) or deep learning (DL), is rapidly revolutionizing cancer treatment by using genomic information for improving diagnosis, prognosis, treatment decision, and drug discovery. Being a result of genetic and molecular changes, it is important to understand cancer’s genomic patterns and profiles. In conventional genomic analyses, common methodologies fail to handle high-dimensional genomic data produced from next-generation sequencing (NGS) and multi-omics platforms; on the other hand, AI approaches excel in detecting intricate patterns from large genomic datasets. This AI system trained from a large public genomic database such as ‘The Cancer Genome Atlas (TCGA),’ ‘Genomic Data Commons (GDC),’ or generally from the ‘Catalogue of Somatic Mutations in Cancer (COSMIC)’ has already facilitated accurate classifications of cancer subtypes and their treatment predictions or discovery of effective biomarkers for treatment of cancer subtypes that are accurate to a great extent. Deep learning from somatic mutation sequences showed an accuracy of approximately 0.98 for clinical biomarkers such as microsatellite instability (MSI), which is a considerably high improvement over other existing methodology. Integration of AI with multi-omics genomic, transcriptomic, proteomic data types further helps to increase efficiency of predictions regarding patient outcomes. Though AI is a revolution in genomic study thereby bringing a revolution in cancer treatment approaches following a detailed precise treatment decision of cancer treatment from an individual’s genomic study followed by inducing a global revolution in cancer treatment true to precision medicine practices around the world.
DOI: http://doi.org/10.5281/zenodo.18066233
