CPL: Detecting Protein Complexes by Propagating Labels on Protein-Protein Interaction Network

Proteins usually bind together to form complexes, which play an important role in cellular activities. Many graph clustering methods have been proposed to identify protein complexes by finding dense regions in protein-protein interaction networks. We present a novel framework （CPL） that detects protein complexes by propagating labels through interactions in a network, in which labels denote complex identifiers. With proper propagation in CPL, proteins in the same complex will be assigned with the same labels. CPL does not make any strong assumptions about the topological structures of the complexes, as in previous methods. Tile CPL algorithm is tested on several publicly available yeast protein-protein interaction networks and compared with several state-of-the-art methods. The results suggest that CPL performs better than the existing methods. An analysis of the functional homogeneity based on a gene ontology analysis shows that the detected complexes of CPL are highly biologically relevant.     

蛋白质功能预测方法研究进展

蛋白质功能预测是后基因组时代生物信息学的研究热点之一。利用计算方法预测蛋白质的功能,可以弥补传统生物实验方法周期长、效率低和成本高等方面不足。首先介绍蛋白质功能预测的研究背景,并从计算角度定义蛋白质功能预测问题;然后,对蛋白质功能预测方法的研究现状进行分析与总结,最后指出已有方法中存在的不足及未来的研究方向。     

网格技术研究与发展趋势综述

简要介绍了网格以及网格计算的概念,阐述了网格技术的国内外研究现状及其应用,并讨论分析了网格技术研究中存在的问题,展望了网格技术的发展趋势.     

植物抗性基因识别中样本选择的一种新方法

植物抗性基因识别中的从头预测方法可以看作机器学习中的分类问题。通常情况下,一个分类器的训练需要正确标记的正例和反例。然而,抗性基因识别中可用的信息仅有少数人工标记的抗性基因,且不具有抗性功能的基因也不明确。为了消除由于正例太少和错误的反例带来的抗性基因识别的影响,基于抗性基因和其他基因在蛋白质相互作用网中的距离,提出了一种新的样本选择方法,并对提出的样本选择方法和通常样本选择方法分别在四种分类器上进行了10倍交叉验证。结果表明,文中方法的SN值平均提高了6.9%,SP值平均提高了13.1%。因此,就敏感性和特异性而言,提出的方法获得了更高效、更可靠的结果。