Identifying protein complexes based on the integration of PPI network and gene expression data.

Q4 Health Professions

International Journal of Bioinformatics Research and Applications Pub Date : 2015-01-01 DOI:10.1504/IJBRA.2015.067337

Weijie Chen, Min Li, Xuehong Wu, Jianxin Wang

引用次数: 2

Abstract

Identification of protein complexes is crucial to understand principles of cellular organisation and predict protein functions. In this paper, a novel protein complex discovery algorithm IPCIPG is proposed based on the integration of Protein-Protein Interaction network (PPI network) and gene expression data. IPCIPG is a local search algorithm which has two versions: IPCIPG-n for identifying non-overlapping clusters and IPCIPG-o for detecting overlapping clusters. The experimental results on the yeast PPI network show that IPCIPG can identify protein complexes with specific biological meaning more effectively, precisely and comprehensively than six other algorithms: HUNTER, HC-PIN, CMC, SPICi, MOCDE and MCL.

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基于PPI网络和基因表达数据的蛋白质复合物鉴定。

鉴定蛋白质复合物对于理解细胞组织原理和预测蛋白质功能至关重要。本文提出了一种基于蛋白质-蛋白质相互作用网络(protein - protein Interaction network, PPI网络)和基因表达数据集成的蛋白质复合物发现算法IPCIPG。IPCIPG是一种局部搜索算法，有两个版本:IPCIPG-n(用于识别非重叠簇)和IPCIPG-o(用于检测重叠簇)。酵母PPI网络的实验结果表明，与HUNTER、HC-PIN、CMC、SPICi、mode和MCL等6种算法相比，IPCIPG能够更有效、更精确、更全面地识别具有特定生物学意义的蛋白质复合物。

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 Bioinformatics Research and Applications Health Professions-Health Information Management

CiteScore

0.60

自引率

0.00%

发文量

期刊介绍： Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.