Precision medicine aims to empower clinicians to predict the most appropriate course of action for patients with complex diseases like cancer, diabetes, cardiomyopathy, and COVID-19. With a progressive interpretation of the clinical, molecular, and genomic factors at play in diseases, more effective and personalized medical treatments are anticipated for many disorders. Understanding patient’s metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic, and predictive biomarkers and paths ultimately providing optimal and personalized care for diverse, and targeted chronic and acute diseases. In clinical settings, we need to timely model clinical and multi-omics data to find statistical patterns across millions of features to identify underlying biologic pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, and development of new therapies for better patient care. It is important to calculate quantitative phenotype measurements, evaluate variants in unique genes and interpret using ACMG guidelines, find frequency of pathogenic and likely pathogenic variants without disease indicators, and observe autosomal recessive carriers with a phenotype manifestation in metabolome. Next, ensuring security to reconcile noise, we need to build and train machine-learning prognostic models to meaningfully process multisource heterogeneous data to identify high-risk rare variants and make medically relevant predictions. The goal, today, is to facilitate implementation of mainstream precision medicine to improve the traditional symptom-driven practice of medicine, and allow earlier interventions using predictive diagnostics and tailoring better-personalized treatments. We strongly recommend automated implementation of cutting-edge technologies, utilizing machine learning (ML) and artificial intelligence (AI) approaches for the multimodal data aggregation, multifactor examination, development of knowledgebase of clinical predictors for decision support, and best strategies for dealing with relevant ethical issues.

中文翻译：

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通过智能集成临床和多组学数据分析来实践精准医学

精密医学旨在使临床医生能够针对患有癌症，糖尿病，心肌病和COVID-19等复杂疾病的患者预测最合适的治疗方案。随着对疾病中起作用的临床，分子和基因组因素的渐进解释，预期对许多疾病将有更有效和个性化的药物治疗。了解患者的代谢组学和基因组成以及临床数据，将大大有助于确定易感性，诊断性，预后性和预测性生物标志物和路径，最终为各种有针对性的慢性和急性疾病提供最佳和个性化的护理。在临床环境中，我们需要及时对临床和多组学数据进行建模，以发现数百万个特征的统计模式，以识别潜在的生物学途径，可修改的风险因素和可操作的信息，支持早期发现和预防复杂疾病，并开发新的疗法以更好地为患者提供护理。重要的是计算定量表型测量值，评估独特基因中的变体并使用ACMG指南进行解释，找到没有疾病指标的致病性和可能致病性变体的频率，并观察代谢组中具有表型表现的常染色体隐性携带者。接下来，为了确保调和噪声的安全性，我们需要构建和训练机器学习的预测模型，以有意义地处理多源异构数据，以识别高风险的罕见变体并做出医学上的相关预测。今天的目标 旨在促进主流精密医学的实施，以改善传统的症状驱动医学实践，并允许使用预测性诊断和量身定制更好的治疗方法进行早期干预。我们强烈建议自动化实施最先进的技术，利用机器学习（ML）和人工智能（AI）方法进行多模式数据汇总，多因素检查，开发临床预测指标知识库以提供决策支持以及处理相关问题的最佳策略伦理道德问题。