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Detection of protein complexes using a protein ranking algorithm

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2012年第10期80卷 2459-2468页

作者： Zaki, Nazar Berengueres, Jose Efimov, Dmitry UAEU Fac Informat Technol Al Ain U Arab Emirates Moscow MV Lomonosov State Univ Fac Mech & Math Moscow Russia

Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. There is ample evidence that many disease mechanisms involve protein complexes, and being able to predict these complexes is important to the characterization of the relevant disease for diagnostic and treatment purposes. This article introduces a novel method for detecting protein complexes from PPI by using a protein ranking algorithm (ProRank). ProRank quantifies the importance of each protein based on the interaction structure and the evolutionarily relationships between proteins in the network. A novel way of identifying essential proteins which are known for their critical role in mediating cellular processes and constructing protein complexes is proposed and analyzed. We evaluate the performance of ProRank using two PPI networks on two reference sets of protein complexes created from Munich Information Center for Protein Sequence, containing 81 and 162 known complexes, respectively. We compare the performance of ProRank to some of the well known protein complex prediction methods (ClusterONE, CMC, CFinder, MCL, MCode and Core) in terms of precision and recall. We show that ProRank predicts more complexes correctly at a competitive level of precision and recall. The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of the proposed method. Proteins 2012;. (C) 2012 Wiley Periodicals, Inc.

关键词： pagerank algorithm protein-protein interaction essential protein evolutionarily relationships pairwise alignment

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ProRank: A Method for Detecting Protein Complexes 12

ProRank: A Method for Detecting Protein Complexes

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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Zaki, Nazar Berengueres, Jose Efimov, Dmitry UAEU Fac Info Technol Al Ain 17551 U Arab Emirates

ISBN: (纸本)9781450311779

Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. Observations show that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. This paper introduces a novel method for detecting protein-complexes from PPI by using a protein ranking algorithm (ProRank) and incorporating an evolutionary relationships between proteins in the network. The method successfully predicted 57 out of 81 benchmarked protein complexes created from the Munich Information Center for Protein Sequence (MIPS). The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of our proposed method. Datasets, programs and results are available at http://***/nzaki/***

关键词： protein-protein interaction protein complex detection pairwise similarity pagerank algorithm

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A New Approach for Aggregated pagerank Computation via Distributed Randomized algorithms

A New Approach for Aggregated PageRank Computation via Distr...

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50th IEEE Conference of Decision and Control (CDC)/European Control Conference (ECC)

作者： Ishii, Hideaki Tempo, Roberto Bai, Er-Wei Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku 4259 Nagatsuta Cho Yokohama Kanagawa 2268502 Japan Politecn Torino IEIIT CNR I-10129 Turin Italy Univ Iowa Ctr Engn Arts & Sci Iowa City IA 52242 USA

ISBN: (纸本)9781612848013

At Google, the pagerank algorithm helps rankings in search results by providing measures of web page importance. This paper builds upon the distributed randomized approach for this algorithm proposed in our recent works. To reduce computation and communication, we develop a method to systematically aggregate web pages into groups by exploiting the sparsity inherent in the web. Each group computes an aggregated pagerank, which can be distributed among group members. We provide a decentralized scheme for its computation and analyze convergence properties.

关键词： Distributed computation Multi-agent consensus pagerank algorithm Randomization Search engines

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Distributed Randomized pagerank algorithms Based on Web Aggregation over Unreliable Channels 49

Distributed Randomized PageRank Algorithms Based on Web Aggr...

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49th IEEE Conference on Decision and Control (CDC)

作者： Ishii, Hideaki Tempo, Roberto Bai, Er-Wei Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku 4259 Nagatsuta Cho Yokohama Kanagawa 2268502 Japan Politecn Torino EIIT CNR 10129 Turin Italy Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA

ISBN: (纸本)9781424477463

The pagerank algorithm employed at Google assigns a measure of importance to each web page for rankings in search results. In our recent papers, we have proposed a distributed randomized approach for this algorithm, where web pages are treated as agents computing their own pagerank by communicating with linked pages. Here, we focus on the effects of unreliability in the communication and, in particular, model the random data losses as an outcome of Markov chains. By generalizing the aggregated pagerank computation previously developed, we provide a distributed scheme along with analyses on its convergence properties.

关键词： Distributed computation Multi-agent consensus pagerank algorithm Randomization Stochastic matrices Unreliable channels

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A New Approach for Aggregated pagerank Computation via Distributed Randomized algorithms

A New Approach for Aggregated PageRank Computation via Distr...

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IEEE Conference on Decision and Control

作者： Hideaki Ishii Roberto Tempo Er-Wei Bai Department of Computational Intelligence and Systems Science Tokyo Institute of Technology 4259 Nagatsuta-cho Midori-ku Yokohama 226-8502 Japan IEIIT-CNR Politecnico di Torino Corso Duca degli Abruzzi 24 10129 Torino Italy Department of Electrical and Computer Engineering The University of Iowa 4316 Seamans Center for the Engineering Arts and Sciences Iowa City IA 52242-1527 U.S.A.

ISBN: (纸本)9781612848006

关键词： Distributed computation Multi-agent consensus pagerank algorithm Randomization Search engines

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Distributed Randomized algorithms for the pagerank Computation

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2010年第9期55卷 1987-2002页

作者： Ishii, Hideaki Tempo, Roberto Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Yokohama Kanagawa 2268502 Japan Politecn Torino IEIIT CNR I-10129 Turin Italy

In the search engine of Google, the pagerank algorithm plays a crucial role in ranking the search results. The algorithm quantifies the importance of each web page based on the link structure of the web. We first provide an overview of the original problem setup. Then, we propose several distributed randomized schemes for the computation of the pagerank, where the pages can locally update their values by communicating to those connected by links. The main objective of the paper is to show that these schemes asymptotically converge in the mean-square sense to the true pagerank values. A detailed discussion on the close relations to the multi-agent consensus problems is also given.

关键词： Distributed computation multi-agent consensus pagerank algorithm randomization search engines stochastic matrices

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Distributed Randomized pagerank algorithms Based on Web Aggregation over Unreliable Channels

Distributed Randomized PageRank Algorithms Based on Web Aggr...

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2010 49th IEEE Conference on Decision and Control

ISBN: (纸本)9781424477456

关键词： Distributed computation Multi-agent consensus pagerank algorithm Randomization Stochastic matrices Unreliable channels

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Citation analysis of orthopaedic literature;18 major orthopaedic journals compared for Impact Factor and SCImago

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BMC MUSCULOSKELETAL DISORDERS 2010年第1期11卷 4-4页

作者： Siebelt, Michiel Siebelt, Teun Pilot, Peter Bloem, Rolf M. Bhandari, Mohit Poolman, Rudolf W. Reinier de Graaf Groep Dept Orthoped Surg NL-2600 GA Delft Netherlands McMaster Univ Div Orthoped Surg Hamilton ON L8S 4L8 Canada Onze Lieve Vrouw Hosp Dept Orthoped Surg Amsterdam Netherlands

Background: One of the disadvantages of the Impact Factor (IF) is self-citation. The SCImago Journal Rank (SJR) indicator excludes self-citations and considers the quality, rather than absolute numbers, of citations of a journal by other journals. The present study re-evaluated the influence of self-citation on the 2007 IF for 18 major orthopaedic journals and investigated the difference in ranking between IF and SJR. Methods: The journals were analysed for self-citation both overall and divided into a general group (n = 8) and a specialized group (n = 10). Self-cited and self-citing rates, as well as citation densities and IFs corrected for self-citation (cIF), were calculated. The rankings of the 18 journals by IF and by SJR were compared and the absolute difference between these rankings (Delta R) was determined. Results: Specialized journals had higher self-citing rates (p = 0.01, Delta median = 9.50, 95%CI-19.42 to 0.42), higher self-cited rates (p = 0.0004, Delta median = -10.50, 95% CI-15.28 to -5.72) and greater differences between IF and cIF (p = 0.003, Delta median = 3.50, 95%CI -6.1 to 13.1). There was no significant correlation between self-citing rate and IF for both groups (general: r = 0.46, p = 0.27;specialized: r = 0.21, p = 0.56). When the difference in ranking between IF and SJR was compared between both groups, sub-specialist journals were ranked lower compared to their general counterparts (Delta R: p = 0.006, Delta median = 2.0, 95% CI -0.39 to 4.39). Conclusions: Citation analysis shows that specialized orthopaedic journals have specific self-citation tendencies. The correlation between self-cited rate and IF in our sample was large but, due to small sample size, not significant. The SJR excludes self-citations in its calculation and therefore enhances the underestimation in ranking of specialized journals.

关键词： Impact Factor Citation Analysis pagerank algorithm Orthopaedic Literature SCImago Journal Rank

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TC-pagerank algorithm based on topic correlation

TC-PageRank algorithm based on topic correlation

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6th World Congress on Intelligent Control and Automation

作者： Huang, Decai Qi, Huachun Yuan, Yuan Zheng, Yue-Feng Zhejiang Univ Technol Coll Informat Engn Hangzhou 310032 Peoples R China

ISBN: (纸本)1424403316

pagerank algorithm is a famous algorithm to mine the web structure,but it has a drawback of topic-drift. To eliminate the topic-drift of the pagerank algorithm, and after the analysis of existing algorithms, a new algorithm called TC-pagerank algorithm is put forward. The TC-pagerank algorithm is based on, fictitious file vector and correlation measure of cosine. Experimental results illustrate that TC-pagerank algorithm eliminates the topic-drift phenomenon effectively, and thus improves the quality of retrieving.

关键词： hyperlink analysis pagerank algorithm topic correlation web structure mining

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Fragile Link Structure in pagerank Computation

Fragile Link Structure in PageRank Computation

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Joint 48th IEEE Conference on Decision and Control (CDC) / 28th Chinese Control Conference (CCC)

作者： Ishii, Hideaki Tempo, Roberto Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku 4259 Nagatsuta Cho Yokohama Kanagawa 2268502 Japan Politecnico Torino IEIIT CNR I-10129 Turin Italy

ISBN: (纸本)9781424438723

In this paper, we study the so-called pagerank algorithm used at Google that determines the importance of each webpage. We first provide a brief overview of this algorithm. Then, we consider the problem of computing the changes in pagerank values when the web data contains erroneous link information. Based on interval matrix techniques, we develop an efficient centralized algorithm using linear programming. The theoretical results are verified by means of extensive simulations for a small web with 150 pages and a larger web of 1,200 pages.

关键词： Fragile links Interval matrices pagerank algorithm Search engines

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