微计算机信息
微計算機信息
미계산궤신식
CONTROL & AUTOMATION
2010年
6期
188-189,217
,共3页
蛋白交互网络%贪婪算法%蛋白质子网%C++
蛋白交互網絡%貪婪算法%蛋白質子網%C++
단백교호망락%탐람산법%단백질자망%C++
Protein-Protein interaction network%greedy algorithm%protein subnetwork%C++
各种研究结果不断证明,人类各种常见疾病都属于复杂疾病,是由多基因、多因素、遗传和环境共同作用的结果.借助于高通量生物技术的飞速发展,生物学家建立起了蛋白交互网络,如果借助复杂网络研究的方法,从这些网络中找出与疾病相关的蛋白质子网络,将有助于我们更深入地了解生物体的运作机制.本文提出了一种基于贪婪算法的搜索方法,能够自动地搜索整个网络中的子网或模块,并且能够结合芯片数据同时进行T检验来判断子网络对疾病表型的区分能力.通过计算子网的P值,给出该蛋白质子网络的统计显著性值并进行区分能力排序.运行结果表明,本方法不但能够用于发现已知的疾病蛋白,而且能够对未知的蛋白进行预测,结合生物芯片技术,将会对疾病基因的研究提供有价值的信息.
各種研究結果不斷證明,人類各種常見疾病都屬于複雜疾病,是由多基因、多因素、遺傳和環境共同作用的結果.藉助于高通量生物技術的飛速髮展,生物學傢建立起瞭蛋白交互網絡,如果藉助複雜網絡研究的方法,從這些網絡中找齣與疾病相關的蛋白質子網絡,將有助于我們更深入地瞭解生物體的運作機製.本文提齣瞭一種基于貪婪算法的搜索方法,能夠自動地搜索整箇網絡中的子網或模塊,併且能夠結閤芯片數據同時進行T檢驗來判斷子網絡對疾病錶型的區分能力.通過計算子網的P值,給齣該蛋白質子網絡的統計顯著性值併進行區分能力排序.運行結果錶明,本方法不但能夠用于髮現已知的疾病蛋白,而且能夠對未知的蛋白進行預測,結閤生物芯片技術,將會對疾病基因的研究提供有價值的信息.
각충연구결과불단증명,인류각충상견질병도속우복잡질병,시유다기인、다인소、유전화배경공동작용적결과.차조우고통량생물기술적비속발전,생물학가건립기료단백교호망락,여과차조복잡망락연구적방법,종저사망락중조출여질병상관적단백질자망락,장유조우아문경심입지료해생물체적운작궤제.본문제출료일충기우탐람산법적수색방법,능구자동지수색정개망락중적자망혹모괴,병차능구결합심편수거동시진행T검험래판단자망락대질병표형적구분능력.통과계산자망적P치,급출해단백질자망락적통계현저성치병진행구분능력배서.운행결과표명,본방법불단능구용우발현이지적질병단백,이차능구대미지적단백진행예측,결합생물심편기술,장회대질병기인적연구제공유개치적신식.
Previous studies continuously prove that many common human diseases are complex diseases which caused by multiple genes, multiple factors and the combination of the heredity and the environment. Benefited from the rapid development of high throughput biotechnology, biologists established protein interaction networks. It would be helpful for us to understand the mechanism of life if we could find interacting protein subnetworks in these networks which related to diseases using complex network analysis. In this paper, we proposed a search strategy based on greedy algorithm. This strategy is able to automatically search for subnetworks or modules in the whole network, and evaluate the subnetworks' ability of discriminating diseases' phenotypes by performing student t tests. After computing the p values of subnetworks, it sorts the suhnetworks according to their discriminating abilities revealed by t scores. Our results show that the proposed method is not only capable of finding known disease proteins, but also predicting proteins with unknown functions. When combined with biochip technology, this method will provide valuable insights for genetic research of diseases.