Recent advancement of analytical approaches for assessing Ataxia telangiectasia mutated kinase inhibitors in Ataxia telangiectasia: An overview.

Ataxia telangiectasia, one of the rare human autosomal recessive disorders, results from a mutation in the Ataxia telangiectasia mutated kinase (ATM) gene, leading to high radiosensitivity, immunodeficiency, and predisposition to cancer. The eukaryotic DNA is a complex component that undergoes a lot of damage due to several endogenous and exogenous stimuli. These stimuli can cause DNA damage regularly, and the human system does repair mechanisms through a range of signal pathways to overcome. DNA double-strand breaks are effectively repaired through ATM-mediated mechanisms like homologous recombination and non-homologous end-joining. Due to replication stress, cancer cells are prone to DNA damage, making them targets for pharmacological intervention to hinder tumor growth. ATM inhibitors, crucial for disrupting DNA damage response pathways in cancer cells, are clinically significant for cancer mitigation. Integrating artificial intelligence (AI) in healthcare can potentially alleviate the workload of healthcare professionals. Detailed discussions delve into the use of machine learning and deep learning programs for the initial screening and monitoring of ataxia and its types. With this focus, the review discusses basic and advanced analytical and AI techniques used to estimate ATM inhibitors and ataxia diseases. • Ataxia telangiectasia mutated kinase gene heightens radiosensitivity and cancer risk. • Understanding DNA damage repair pathways using ataxia telangiectasia is crucial. • Ataxia telangiectasia mutated kinase inhibition promises in cancer therapy. • Artificial intelligence is helpful to healthcare in ataxia screening and monitoring. [ABSTRACT FROM AUTHOR]