Identifying susceptibility networks for drug-induced non-immune neutropenia.

IF 0.2 4区生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI:10.1504/ijdmb.2015.066339

Kaushal Desai, David Brott, Xiaohua Hu, Anastasia Christianson

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引用次数: 0

Abstract

Systems toxicology, a branch of toxicology that studies drug effects at the level of biological systems, offers exciting opportunities to discover toxicity-related sub-networks using high-throughput technologies. This paper takes a computational approach to systems toxicology and investigates the use of automated signalling path detection for discovery of potential biomarkers of drug-induced non-immune neutropenia. The algorithm utilises a gene expression change measure to mine a large protein interaction network and identify chemical-toxicity signalling paths. Cytoscape-based analysis of detected signalling paths with statistically significant path expression scores reveals 'hub' proteins and a smaller sub-network of path proteins. The importance of 'hub' and drug-toxicity signalling path proteins in haematological and apoptotic signal transduction networks is investigated in order to understand the value of automated signalling path detection approach.

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确定药物诱导的非免疫性中性粒细胞减少的易感性网络。

系统毒理学是毒理学的一个分支，研究生物系统水平上的药物作用，它为使用高通量技术发现与毒性相关的子网络提供了令人兴奋的机会。本文采用系统毒理学的计算方法，并研究了自动信号路径检测用于发现药物诱导的非免疫性中性粒细胞减少症的潜在生物标志物的使用。该算法利用基因表达变化测量来挖掘大型蛋白质相互作用网络并识别化学毒性信号通路。基于细胞壁的检测信号通路分析具有统计学意义的通路表达分数揭示了“枢纽”蛋白和更小的通路蛋白子网络。研究了“枢纽”和药物毒性信号通路蛋白在血液学和凋亡信号转导网络中的重要性，以了解自动信号通路检测方法的价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Data Mining and Bioinformatics 生物-数学与计算生物学

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Mining bioinformatics data is an emerging area at the intersection between bioinformatics and data mining. The objective of IJDMB is to facilitate collaboration between data mining researchers and bioinformaticians by presenting cutting edge research topics and methodologies in the area of data mining for bioinformatics. This perspective acknowledges the inter-disciplinary nature of research in data mining and bioinformatics and provides a unified forum for researchers/practitioners/students/policy makers to share the latest research and developments in this fast growing multi-disciplinary research area.