用于毒理学和药物发现应用的社区评审生物网络模型。

Gene regulation and systems biology Pub Date : 2016-07-12 eCollection Date: 2016-01-01 DOI:10.4137/GRSB.S39076
Aishwarya Alex Namasivayam, Alejandro Ferreiro Morales, Ángela María Fajardo Lacave, Aravind Tallam, Borislav Simovic, David Garrido Alfaro, Dheeraj Reddy Bobbili, Florian Martin, Ganna Androsova, Irina Shvydchenko, Jennifer Park, Jorge Val Calvo, Julia Hoeng, Manuel C Peitsch, Manuel González Vélez Racero, Maria Biryukov, Marja Talikka, Modesto Berraquero Pérez, Neha Rohatgi, Noberto Díaz-Díaz, Rajesh Mandarapu, Rubén Amián Ruiz, Sergey Davidyan, Shaman Narayanasamy, Stéphanie Boué, Svetlana Guryanova, Susana Martínez Arbas, Swapna Menon, Yang Xiang
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引用次数: 0

摘要

生物网络模型通过描述生物过程调控机制之间的关系,为理解疾病提供了一个框架。众包可以有效地从具有不同专业知识的广大受众那里收集反馈。在网络验证挑战赛中,科学家们验证并增强了一组与肺部和慢性阻塞性肺病相关的 46 个生物网络。这些网络使用生物表达语言构建,包含每个节点和边缘的详细信息,包括来自文献的支持证据。对公共转录组学数据进行网络评分后,推断出与测量结果相匹配的机制和网络子集的扰动。这些结果基于可计算的网络方法,可用于识别疾病中激活的新机制,定量比较不同的治疗方法和时间点,并允许对低信号数据进行评估。这些网络会定期接受群众的验证,以维护一套最新的毒理学和药物发现应用网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications.

Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications.

Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications.

Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications.

Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications.

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