治疗目标预测中网络建模的一些观点。

IF 2.3 Q3 ENGINEERING, BIOMEDICAL
Biomedical Engineering and Computational Biology Pub Date : 2013-02-21 eCollection Date: 2013-01-01 DOI:10.4137/BECB.S10793
Reka Albert, Bhaskar DasGupta, Nasim Mobasheri
{"title":"治疗目标预测中网络建模的一些观点。","authors":"Reka Albert, Bhaskar DasGupta, Nasim Mobasheri","doi":"10.4137/BECB.S10793","DOIUrl":null,"url":null,"abstract":"<p><p>Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities. </p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"5 ","pages":"17-24"},"PeriodicalIF":2.3000,"publicationDate":"2013-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147778/pdf/","citationCount":"0","resultStr":"{\"title\":\"Some perspectives on network modeling in therapeutic target prediction.\",\"authors\":\"Reka Albert, Bhaskar DasGupta, Nasim Mobasheri\",\"doi\":\"10.4137/BECB.S10793\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities. </p>\",\"PeriodicalId\":42484,\"journal\":{\"name\":\"Biomedical Engineering and Computational Biology\",\"volume\":\"5 \",\"pages\":\"17-24\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2013-02-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147778/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Engineering and Computational Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4137/BECB.S10793\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2013/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Engineering and Computational Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4137/BECB.S10793","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2013/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

摘要

药物靶点识别对制药公司来说具有重大的商业利益,与治疗靶点识别相关的研究成果也不胜枚举。这一领域的跨学科研究涉及生物网络社区和图算法社区。典型的治疗目标识别问题的关键步骤包括综合或推断与疾病相关的复杂相互作用网络,将该网络与疾病的特定行为联系起来,并预测哪些成分是该行为的关键媒介。所有这些步骤都涉及图论或图算法方面。在这一视角中,我们将从建模和算法的角度来探讨治疗目标的识别,并重点介绍迄今为止关注较少的一些算法进展,希望能加强这两个研究领域之间的联系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Some perspectives on network modeling in therapeutic target prediction.

Some perspectives on network modeling in therapeutic target prediction.

Some perspectives on network modeling in therapeutic target prediction.

Some perspectives on network modeling in therapeutic target prediction.

Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities.

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
自引率
0.00%
发文量
1
审稿时长
8 weeks
×
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