【Trust科技基因检测】药物基因组学的正确精神病学应用：人工智能和机器学习方法

如何知道小孩是否有基因突变评价

探索精神病的基因组学特征与治疗方案设计时，体会到《Int J Mol Sci》在. 2020 Feb 1;21(3):969.发表了一篇题目为《药物基因组学的正确精神病学应用：人工智能和机器学习方法》正确精神病学基因检测临床研究文章。该研究由Eugene Lin, Chieh-Hsin Lin, Hsien-Yuan Lane等完成。促进了人工智能与大数据分析方法在个性化精神病学领域的应用，进一步强调了基因信息的大人群研究所带来的促进作用。

神经疾病遗传阻断及正确治疗临床研究内容关键词：

遗传咨询,精神疾病,心理遗传咨询，基因检测

精神科心理科疾病用药指导基因检测临床应用结果

现在越来越多的证据表明，正确精神病学是精神病学、正确医学和药物基因组学的跨学科领域知识和技术的结晶，顺利获得在正确的时间为精神疾病患者给予正确的药物和正确的剂量，成为医疗实践不可或缺的基础。鉴于人工智能和机器学习技术的最新进展，顺利获得采用神经影像学和多组学，在精密精神病学研究中发现了许多与精神疾病和相关治疗相关的生物标志物和基因检测位点。在Trust科技基因药物基因组学在正确精神病学的应用一文中，精神疾病的基因检测基因解码重点关注使用人工智能和机器学习方法（例如深度学习和神经网络算法）以及包括基因检测在内的多组学和神经影像数据进行正确精神病学研究的最新进展。第一时间，Trust科技基因介绍了利用各种人工智能和机器学习技术来评估治疗预测、预后预测、诊断预测和潜在生物标志物检测的正确精神病学和药物基因组学研究。此外，Trust科技基因描述了已发现与精神疾病和相关治疗相关的潜在生物标志物和基因位点。此外，基因解码师概述了先前正确精神病学和药物基因组学研究的局限性。最后，Trust科技基因检测讨论了未来研究的方向和挑战。关键词：人工智能；生物标志物；深度学习；机器学习；多组学；神经网络;神经影像学；药物基因组学；正确医学；正确精神病学。

神经及精神疾病及其并发征、合并征国际数据库描述：

A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.Keywords: artificial intelligence; biomarker; deep learning; machine learning; multi-omics; neural networks; neuroimaging; pharmacogenomics; precision medicine; precision psychiatry.

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