【佳學(xué)基因檢測】藥物基因組學(xué)的正確精神病學(xué)應(yīng)用：人工智能和機(jī)器學(xué)習(xí)方法

如何知道小孩是否有基因突變評價(jià)

探索精神病的基因組學(xué)特征與治療方案設(shè)計(jì)時(shí)，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(xué)的正確精神病學(xué)應(yīng)用：人工智能和機(jī)器學(xué)習(xí)方法》正確精神病學(xué)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(jìn)了人工智能與大數(shù)據(jù)分析方法在個(gè)性化精神病學(xué)領(lǐng)域的應(yīng)用，進(jìn)一步強(qiáng)調(diào)了基因信息的大人群研究所帶來的促進(jìn)作用。

神經(jīng)疾病遺傳阻斷及正確治療臨床研究內(nèi)容關(guān)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(dǎo)基因檢測臨床應(yīng)用結(jié)果

現(xiàn)在越來越多的證據(jù)表明，正確精神病學(xué)是精神病學(xué)、正確醫(yī)學(xué)和藥物基因組學(xué)的跨學(xué)科領(lǐng)域知識和技術(shù)的結(jié)晶，通過在正確的時(shí)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(shí)踐不可或缺的基礎(chǔ)。鑒于人工智能和機(jī)器學(xué)習(xí)技術(shù)的賊新進(jìn)展，通過采用神經(jīng)影像學(xué)和多組學(xué)，在精密精神病學(xué)研究中發(fā)現(xiàn)了許多與精神疾病和相關(guān)治療相關(guān)的生物標(biāo)志物和基因檢測位點(diǎn)。在佳學(xué)基因藥物基因組學(xué)在正確精神病學(xué)的應(yīng)用一文中，精神疾病的基因檢測基因解碼重點(diǎn)關(guān)注使用人工智能和機(jī)器學(xué)習(xí)方法（例如深度學(xué)習(xí)和神經(jīng)網(wǎng)絡(luò)算法）以及包括基因檢測在內(nèi)的多組學(xué)和神經(jīng)影像數(shù)據(jù)進(jìn)行正確精神病學(xué)研究的賊新進(jìn)展。首先，佳學(xué)基因介紹了利用各種人工智能和機(jī)器學(xué)習(xí)技術(shù)來評估治療預(yù)測、預(yù)后預(yù)測、診斷預(yù)測和潛在生物標(biāo)志物檢測的正確精神病學(xué)和藥物基因組學(xué)研究。此外，佳學(xué)基因描述了已發(fā)現(xiàn)與精神疾病和相關(guān)治療相關(guān)的潛在生物標(biāo)志物和基因位點(diǎn)。此外，基因解碼師概述了先前正確精神病學(xué)和藥物基因組學(xué)研究的局限性。賊后，佳學(xué)基因檢測討論了未來研究的方向和挑戰(zhàn)。關(guān)鍵詞：人工智能；生物標(biāo)志物；深度學(xué)習(xí)；機(jī)器學(xué)習(xí)；多組學(xué)；神經(jīng)網(wǎng)絡(luò);神經(jīng)影像學(xué)；藥物基因組學(xué)；正確醫(yī)學(xué)；正確精神病學(xué)。

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.