【佳學基因檢測】藥物基因組學的正確精神病學應用：人工智能和機器學習方法

如何知道小孩是否有基因突變評價

探索精神病的基因組學特征與治療方案設計時，體會到《Int J Mol Sci》在. 2020 Feb 1;21(3):969.發(fā)表了一篇題目為《藥物基因組學的正確精神病學應用：人工智能和機器學習方法》正確精神病學基因檢測臨床研究文章。該研究由Eugene Lin, Chieh-Hsin Lin, Hsien-Yuan Lane等完成。促進了人工智能與大數(shù)據(jù)分析方法在個性化精神病學領域的應用，進一步強調(diào)了基因信息的大人群研究所帶來的促進作用。

神經(jīng)疾病遺傳阻斷及正確治療臨床研究內(nèi)容關鍵詞：

遺傳咨詢,精神疾病,心理遺傳咨詢，基因檢測

精神科心理科疾病用藥指導基因檢測臨床應用結果

現(xiàn)在越來越多的證據(jù)表明，正確精神病學是精神病學、正確醫(yī)學和藥物基因組學的跨學科領域知識和技術的結晶，通過在正確的時間為精神疾病患者提供正確的藥物和正確的劑量，成為醫(yī)療實踐不可或缺的基礎。鑒于人工智能和機器學習技術的賊新進展，通過采用神經(jīng)影像學和多組學，在精密精神病學研究中發(fā)現(xiàn)了許多與精神疾病和相關治療相關的生物標志物和基因檢測位點。在佳學基因藥物基因組學在正確精神病學的應用一文中，精神疾病的基因檢測基因解碼重點關注使用人工智能和機器學習方法（例如深度學習和神經(jīng)網(wǎng)絡算法）以及包括基因檢測在內(nèi)的多組學和神經(jīng)影像數(shù)據(jù)進行正確精神病學研究的賊新進展。首先，佳學基因介紹了利用各種人工智能和機器學習技術來評估治療預測、預后預測、診斷預測和潛在生物標志物檢測的正確精神病學和藥物基因組學研究。此外，佳學基因描述了已發(fā)現(xiàn)與精神疾病和相關治療相關的潛在生物標志物和基因位點。此外，基因解碼師概述了先前正確精神病學和藥物基因組學研究的局限性。賊后，佳學基因檢測討論了未來研究的方向和挑戰(zhàn)。關鍵詞：人工智能；生物標志物；深度學習；機器學習；多組學；神經(jīng)網(wǎng)絡;神經(jīng)影像學；藥物基因組學；正確醫(yī)學；正確精神病學。

神經(jīng)及精神疾病及其并發(fā)征、合并征國際數(shù)據(jù)庫描述：

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.