Division of Nephrology and Hypertension, Department of Medicine, New York Presbyterian Hospital-Weill Cornell Medical College, 318 West 100th Street, Box 8D, New York, NY, 10025, USA.
Curr Hypertens Rep. 2020 Aug 27;22(9):70. doi: 10.1007/s11906-020-01068-8.
This review a highlights that to use artificial intelligence (AI) tools effectively for hypertension research, a new foundation to further understand the biology of hypertension needs to occur by leveraging genome and RNA sequencing technology and derived tools on a broad scale in hypertension.
For the last few years, progress in research and management of essential hypertension has been stagnating while at the same time, the sequencing of the human genome has been generating many new research tools and opportunities to investigate the biology of hypertension. Cancer research has applied modern tools derived from DNA and RNA sequencing on a large scale, enabling the improved understanding of cancer biology and leading to many clinical applications. Compared with cancer, studies in hypertension, using whole genome, exome, or RNA sequencing tools, total less than 2% of the number cancer studies. While true, sequencing the genome of cancer tissue has provided cancer research an advantage, DNA and RNA sequencing derived tools can also be used in hypertension to generate new understanding how complex protein network, in non-cancer tissue, adapts and learns to be effective when for example, somatic mutations or environmental inputs change the gene expression profiles at different network nodes. The amount of data and differences in clinical condition classification at the individual sample level might be of such magnitude to overwhelm and stretch comprehension. Here is the opportunity to use AI tools for the analysis of data streams derived from DNA and RNA sequencing tools combined with clinical data to generate new hypotheses leading to the discovery of mechanisms and potential target molecules from which drugs or treatments can be developed and tested. Basic and clinical research taking advantage of new gene sequencing-based tools, to uncover mechanisms how complex protein networks regulate blood pressure in health and disease, will be critical to lift hypertension research and management from its stagnation. The use of AI analytic tools will help leverage such insights. However, applying AI tools to vast amounts of data that certainly exist in hypertension, without taking advantage of new gene sequencing-based research tools, will generate questionable results and will miss many new potential molecular targets and possibly treatments. Without such approaches, the vision of precision medicine for hypertension will be hard to accomplish and most likely not occur in the near future.
本文强调，为了有效地将人工智能（AI）工具应用于高血压研究，需要利用基因组和 RNA 测序技术以及广泛的高血压衍生工具，进一步了解高血压的生物学。
在过去的几年中，原发性高血压的研究和管理进展一直停滞不前，而与此同时，人类基因组测序产生了许多新的研究工具和机会来研究高血压的生物学。癌症研究已经大规模地应用了源自 DNA 和 RNA 测序的现代工具，使人们对癌症生物学有了更好的理解，并带来了许多临床应用。与癌症相比，使用全基因组、外显子组或 RNA 测序工具的高血压研究不到癌症研究数量的 2%。虽然确实，对癌症组织的基因组进行测序为癌症研究提供了优势，但也可以在高血压中使用 DNA 和 RNA 测序衍生工具来生成新的认识，了解当体细胞突变或环境输入改变不同网络节点的基因表达谱时，非癌症组织中的复杂蛋白质网络如何适应和学习变得有效。个体样本水平的数据量和临床条件分类的差异可能大到难以理解。这是使用 AI 工具分析源自 DNA 和 RNA 测序工具与临床数据相结合的数据的机会，这些工具可以生成新的假设，从而发现机制和潜在的靶分子，从中可以开发和测试药物或治疗方法。利用新的基于基因测序的工具进行基础和临床研究，以揭示复杂蛋白质网络在健康和疾病中调节血压的机制，对于摆脱高血压研究和管理的停滞状态至关重要。利用 AI 分析工具将有助于利用这些见解。然而，如果不利用新的基于基因测序的研究工具，而只是将 AI 工具应用于高血压中肯定存在的大量数据，将会产生有问题的结果，并且会错过许多新的潜在分子靶标和可能的治疗方法。如果没有这样的方法，高血压的精准医疗愿景将难以实现，而且很可能在不久的将来不会实现。
