A Survey on Classification Analysis for Cancer Genomics: Limitations and Novel Opportunity in the Era of Cancer Classification and Target Therapies

Research Abstract

Advanced machine learning approaches are qualified for recognizing the too composite patterns in the massive datasets. We provide a perspective technical survey analysis in machine learning (ML), and deep learning (DL) approaches for genome analysis. It's quickly rising applications related to cancer diseases such as cancer diagnosis or subtypes of cancer through omics input data. It discusses effective approaches in the fields of genomics regulatory, pathogenicity, and variant calling. Moreover, the representation of ML's potential benefits due to the several technological platforms involved in its diagnosis, prognosis, and treatment. We concentrate on the most up-to-date knowledge of cancer classification models, targeted therapy, and define how genetic mutations inspire targeted therapy's responsiveness and highlight the different related issues in this era of precision medicine. Finally, we disuse limitations of the different approaches and hopeful ways of upcoming research in targeted therapy.

Research Keywords

Deep Learning, Genome Analysis, Precision Medicine, Cancer Classification Models, Classification Models, Omics