Journal article
Identifying susceptibility networks for drug-induced non-immune neutropenia
International journal of data mining and bioinformatics, v 11(1), pp 102-114
01 Jan 2015
PMID: 26255378
Featured in Collection : UN Sustainable Development Goals @ Drexel
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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Details
- Title
- Identifying susceptibility networks for drug-induced non-immune neutropenia
- Creators
- Kaushal Desai - AstraZeneca Pharmaceuticals, Wilmington, DE 19850, USADavid Brott - AstraZeneca Pharmaceuticals, Wilmington, DE 19850, USAXiaohua Hu - Drexel UniversityAnastasia Christianson - AstraZeneca Pharmaceuticals, Wilmington, DE 19850, USA
- Publication Details
- International journal of data mining and bioinformatics, v 11(1), pp 102-114
- Publisher
- Inderscience Enterprises Ltd
- Number of pages
- 13
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000352841400005
- Scopus ID
- 2-s2.0-84919673853
- Other Identifier
- 991019167614404721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Mathematical & Computational Biology