Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Artificial Intelligence and Machine Learning in Precision and Genomic Medicine

Version 1 : Received: 30 September 2021 / Approved: 1 October 2021 / Online: 1 October 2021 (11:41:27 CEST)

How to cite: Quazi, S. Artificial Intelligence and Machine Learning in Precision and Genomic Medicine. Preprints 2021, 2021100011. https://doi.org/10.20944/preprints202110.0011.v1 Quazi, S. Artificial Intelligence and Machine Learning in Precision and Genomic Medicine. Preprints 2021, 2021100011. https://doi.org/10.20944/preprints202110.0011.v1

Abstract

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision-making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of data sets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Keywords

Machine Learning; Precision Medicine; Genomic Medicine; Therapeutic; Artificial Intelligence

Subject

Computer Science and Mathematics, Mathematical and Computational Biology

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