文章基本信息

标题：Predictive Genomics: A Post-genomic Integrated Approach to Analyse Biological Signatures of Radiation Exposure
本地全文：下载
作者：Manikandan Jayapal ; Swaminathan Sethu ; Dimphy Zeegers 等
期刊名称：Defence Science Journal
印刷版ISSN：0976-464X
出版年度：2011
卷号：61
期号：2
页码：133-137
DOI：10.14429/dsj.61.833
语种：English
出版社：Defence Scientific Information & Documentation Centre
摘要：The ultimate objective of radiation research is to link human diseases with the altered gene expression that underlie them and the exposure type and level that caused them. However, this has remained a daunting task for radiation biologists to indent genomic signatures of radiation exposures. Transcriptomic analysis of the cells can reveal the biochemical or biological mechanisms affected by radiation exposures. Predictive genomics has revolutionised how researchers can study the molecular basis of adverse effects of exposure to ionising radiation. It is expected that the new field will find efficient and high-throughput means to delineate mechanisms of action, risk assessment, identify and understand basic mechanisms that are critical to disease progression, and predict dose levels of radiation exposure. Previously, we have shown that cells responding to environmental toxicants through biological networks that are engaged in the regulation of molecular functions such as DNA repair and oxidative stress. To illustrate radiation genomics as an effective tool in biological dosimetry, an overview has been provided of some of the current radiation genomics landscapes as well as potential future systems to integrate the results of radiation response profiling across multiple biological levels in to a broad consensus picture. Predictive genomics represents a promising approach to high-throughput radiation biodosimetry. Defence Science Journal, 2011, 61(2), pp.133-137 , DOI:http://dx.doi.org/10.14429/dsj.61.833
关键词：Biomarkers, ionising radiation, genomics, microarray, genomic integration, biodosimetry, predictive genomics, radiation research, genomic signatures, transcriptomic analysis