GenLab Biosolutions Private Limited, Bangalore, Karnataka, 560043, India.
Department of Biomedical Sciences, School of Life Sciences, Anglia Ruskin University, Cambridge, UK.
Med Oncol. 2022 Jun 15;39(8):120. doi: 10.1007/s12032-022-01711-1.
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 datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.
精准医疗在医疗保健中的进步已经超越了传统的基于症状的治疗过程，通过改进诊断和定制更有效的治疗方法，实现了疾病的早期风险预测。有必要仔细观察患者的整体数据以及广泛的因素，观察和区分疾病患者和相对健康的人，以采取最适合精准医疗的路径，从而改善可以预示健康变化的生物指标的预测。精准医学和基因组医学结合人工智能有可能改善患者的医疗保健。具有较少常见治疗反应或独特医疗需求的患者正在使用基因组医学技术。人工智能通过先进的计算和推理提供见解，使系统能够进行推理和学习，同时增强医生的决策能力。许多细胞特征，包括基因上调、蛋白质与核酸结合以及剪接，都可以进行高通量测量，并用作预测模型的训练目标。随着更广泛的数据集和现代计算机技术（如机器学习）的可用性的提高，研究人员可以开创基因组医学的新时代。本文综述了 ML 算法在精准和基因组医学中的贡献。
