基于网络的人工智能方法促进个性化精神病学的发展

IF 1.6 3区 医学 Q3 GENETICS & HEREDITY
Sivanesan Rajan, Emanuel Schwarz
{"title":"基于网络的人工智能方法促进个性化精神病学的发展","authors":"Sivanesan Rajan,&nbsp;Emanuel Schwarz","doi":"10.1002/ajmg.b.32997","DOIUrl":null,"url":null,"abstract":"<p>Psychiatric disorders have a complex biological underpinning likely involving an interplay of genetic and environmental risk contributions. Substantial efforts are being made to use artificial intelligence approaches to integrate features within and across data types to increase our etiological understanding and advance personalized psychiatry. Network science offers a conceptual framework for exploring the often complex relationships across different levels of biological organization, from cellular mechanistic to brain-functional and phenotypic networks. Utilizing such network information effectively as part of artificial intelligence approaches is a promising route toward a more in-depth understanding of illness biology, the deciphering of patient heterogeneity, and the identification of signatures that may be sufficiently predictive to be clinically useful. Here, we present examples of how network information has been used as part of artificial intelligence within psychiatry and beyond and outline future perspectives on how personalized psychiatry approaches may profit from a closer integration of psychiatric research, artificial intelligence development, and network science.</p>","PeriodicalId":7673,"journal":{"name":"American Journal of Medical Genetics Part B: Neuropsychiatric Genetics","volume":"195 8","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ajmg.b.32997","citationCount":"0","resultStr":"{\"title\":\"Network-based artificial intelligence approaches for advancing personalized psychiatry\",\"authors\":\"Sivanesan Rajan,&nbsp;Emanuel Schwarz\",\"doi\":\"10.1002/ajmg.b.32997\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Psychiatric disorders have a complex biological underpinning likely involving an interplay of genetic and environmental risk contributions. Substantial efforts are being made to use artificial intelligence approaches to integrate features within and across data types to increase our etiological understanding and advance personalized psychiatry. Network science offers a conceptual framework for exploring the often complex relationships across different levels of biological organization, from cellular mechanistic to brain-functional and phenotypic networks. Utilizing such network information effectively as part of artificial intelligence approaches is a promising route toward a more in-depth understanding of illness biology, the deciphering of patient heterogeneity, and the identification of signatures that may be sufficiently predictive to be clinically useful. Here, we present examples of how network information has been used as part of artificial intelligence within psychiatry and beyond and outline future perspectives on how personalized psychiatry approaches may profit from a closer integration of psychiatric research, artificial intelligence development, and network science.</p>\",\"PeriodicalId\":7673,\"journal\":{\"name\":\"American Journal of Medical Genetics Part B: Neuropsychiatric Genetics\",\"volume\":\"195 8\",\"pages\":\"\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ajmg.b.32997\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American Journal of Medical Genetics Part B: Neuropsychiatric Genetics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ajmg.b.32997\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Medical Genetics Part B: Neuropsychiatric Genetics","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ajmg.b.32997","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0

摘要

精神病具有复杂的生物学基础,可能涉及遗传和环境风险的相互作用。目前,人们正大力使用人工智能方法来整合数据类型内和数据类型间的特征,以加深我们对病因学的理解,推动个性化精神病学的发展。网络科学提供了一个概念框架,用于探索从细胞机理到大脑功能和表型网络等不同层次的生物组织之间往往十分复杂的关系。作为人工智能方法的一部分,有效利用此类网络信息是一条大有可为的途径,有助于更深入地了解疾病的生物学特性、解读患者的异质性,以及识别足以预测临床症状的特征。在此,我们将举例说明如何将网络信息用作精神病学内外人工智能的一部分,并概述个性化精神病学方法如何从精神病学研究、人工智能开发和网络科学的更紧密结合中获益的未来前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Network-based artificial intelligence approaches for advancing personalized psychiatry

Network-based artificial intelligence approaches for advancing personalized psychiatry

Psychiatric disorders have a complex biological underpinning likely involving an interplay of genetic and environmental risk contributions. Substantial efforts are being made to use artificial intelligence approaches to integrate features within and across data types to increase our etiological understanding and advance personalized psychiatry. Network science offers a conceptual framework for exploring the often complex relationships across different levels of biological organization, from cellular mechanistic to brain-functional and phenotypic networks. Utilizing such network information effectively as part of artificial intelligence approaches is a promising route toward a more in-depth understanding of illness biology, the deciphering of patient heterogeneity, and the identification of signatures that may be sufficiently predictive to be clinically useful. Here, we present examples of how network information has been used as part of artificial intelligence within psychiatry and beyond and outline future perspectives on how personalized psychiatry approaches may profit from a closer integration of psychiatric research, artificial intelligence development, and network science.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.90
自引率
7.10%
发文量
40
审稿时长
4-8 weeks
期刊介绍: Neuropsychiatric Genetics, Part B of the American Journal of Medical Genetics (AJMG) , provides a forum for experimental and clinical investigations of the genetic mechanisms underlying neurologic and psychiatric disorders. It is a resource for novel genetics studies of the heritable nature of psychiatric and other nervous system disorders, characterized at the molecular, cellular or behavior levels. Neuropsychiatric Genetics publishes eight times per year.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信