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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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Stages of User Engagement on Social Commerce Platforms: Analysis with the Navigational Clickstream Data

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INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE 2019年第2期23卷 179-211页

作者： Kumar, Ashish Salo, Jari Li, Hongxiu Aalto Univ Sch Business Dept Mkt Espoo Finland Univ Helsinki Mkt Helsinki Finland Tampere Univ Technol Dept Ind & Informat Management Tampere Finland

Social commerce platforms have gained prominence in e-commerce, as social media has become an integral part of users' online activities. Therefore, firms have been either developing or utilizing social commerce platforms to increase user engagement by adding social shopping facility onto their electronic commerce platforms. However, managing user engagement and user interaction becomes complex when e-commerce platforms are transformed into social commerce platforms. In this study, we operationalize four distinct stages of the social commerce platform, namely, social identification, social interaction, social shopping, and transaction based on salience theory. Using clickstream data, we empirically measure user engagement in these four states by modeling users' incidence and time spent. Drawing from the pagerank algorithm, we capture the importance of ranking and distance on user engagement. The model also accounts for the effects of situational variables such as weekend;holiday;time of day;and user characteristics, such as gender and social media setting. Our results suggest that ranking and distance have significant effects on users' incidence as well as time spent on social commerce platforms. The insights from this study can be helpful in designing the social commerce platform effectively using only the customers' path navigational clickstream data from the parent social commerce platform.

关键词： Clickstream data Dijkstra's shortest path algorithm hierarchical Bayesian method multivariate type-2 Tobit online communities online platforms online shopping pagerank algorithm social commerce platforms

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Exploring time factors in measuring the scientific impact of scholars

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SCIENTOMETRICS 2017年第3期112卷 1301-1321页

作者： Zhang, Jun Ning, Zhaolong Bai, Xiaomei Kong, Xiangjie Zhou, Jinmeng Xia, Feng Dalian Univ Technol Sch Software Dalian 116620 Peoples R China Dalian Univ Technol Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China Anshan Normal Univ Ctr Comp Anshan 114007 Peoples R China

Taking advantage of the easy access to the rich and massive scholarly data, more and more researchers are focusing on the studies of analyzing and utilizing the scholarly big data. Among them, evaluating the scientific impact of scholars has significant importance. Measuring the scientific impact of scholars can not only provide basis for the applications of academic foundations and awards, but also shed light on the research directions for scholars. Currently, citation based methods and network based metrics are the most commonly used ways to evaluate the scientific impact. However, these approaches ignore several important facts, i.e. the dynamics of citations and the initial qualities of different articles. To alleviate the shortcomings of them, we propose a Time-aware Ranking algorithm (TRank) to evaluate the impact of scholars. Due to scholars' sustainable supreme concerns of academic innovations, the TRank algorithm gives more credits to the newly published scholarly papers as well as their references according to the representative time functions. Our method also combines the merits of random walk algorithms and heterogeneous network topology, i.e. the mutual influences among different scholarly entities in heterogeneous academic networks. To validate the suitable time function for TRank algorithm and explore its performance, we construct the experiments on two real datasets: (1) Digitial Bibliography and Library Project, and (2) American Physical Society. The experimental results demonstrate that our algorithm outperforms other methods in selecting outstanding scholars and the evaluation results on the overall impact of scholars.

关键词： Heterogeneous network Scholarly big data pagerank algorithm Random walk algorithm

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Analysis of Hyperlinks and the Ant algorithm for Calculating the Ranks of Web Pages

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2007年第1期41卷 44-53页

作者： Alyguliev, R. M. Natl Acad Sci Azerbaijan Inst Informat Technol Ul F Agaeva 9 AZ-1141 Baku Azerbaijan

A survey of existing algorithms used in the analysis of hyperlinks in the Web, such as pagerank, HITS, and modified variants of these algorithms, is presented. Particular attention is devoted to the quality and acceleration of the ranking of the results of a search. An "ant algorithm" is proposed as a means of accelerating the computation of the ranks of Web pages.

关键词： Web structure mining pagerank algorithm HITS algorithm ant algorithm

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BioMDSum: An Effective Hybrid Biomedical Multi-Document Summarization Method Based on pagerank and Longformer Encoder-Decoder

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IEEE ACCESS 2024年 12卷 188013-188031页

作者： Aftiss, Azzedine Lamsiyah, Salima El Alaoui, Said Ouatik Schommer, Christoph Ibn Tofail Univ Natl Sch Appl Sci Engn Sci Lab Kenitra 14000 Morocco Univ Luxembourg Fac Sci Technol & Med Dept Comp Sci L-4364 Esch Sur Alzette Luxembourg

Biomedical multi-document summarization (BioMDSum) involves automatically generating concise and informative summaries from collections of related biomedical documents. While extractive summarization methods have shown promise, they often produce incoherent summaries. Onethe other hand, fully abstractive methods yield coherent summaries but demand extensive training datasets and computational resources due to the typically lengthy nature of biomedical documents. Toeaddress these challenges, wepropose a hybrid summarization method that combines the strengths of both approaches. The proposed method consists of two main phases: (i) an extractive summarization phase that uses k-means clustering to group similar sentences based on their cosine similarity between embeddings generated by the sentence-BERT model, followed by the pagerank algorithm for sentence scoring and selection;and (ii) an abstractive summarization phase that fine-tunes a Longform Encoder-Decoder (LED) transformer model to generate a concise and coherent summary from the sentences selected during the extractive phase. We conducted several experiments on the standard biomedical multi-document summarization datasets Cochrane and MS<^>2. The results demonstrate that the proposed method is competitive and outperforms recent state-of-the-art systems based on ROUGE evaluation measures. Specifically, our model achieved ROUGE-1, ROUGE-2, ROUGE-L, BERTScore, and METEOR scores of 29.41%, 6.57%, 18.31%, 85.95%, and 22.15% on the Cochrane dataset, and 28.79%, 8.22%, 17.93%, 85.51%, and 25.17% on the MS<^>2 dataset, respectively. Furthermore, aneablation analysis shows that integrating extractive and abstractive phases in our hybrid summarization method enhances the overall performance of the proposed approach.

关键词： Biomedical multi-document summarization hybrid summarization K-means clustering sentence-BERT pagerank algorithm longformer encoder-decoder Biomedical multi-document summarization hybrid summarization K-means clustering sentence-BERT pagerank algorithm longformer encoder-decoder

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Inferring influenza global transmission networks without complete phylogenetic information

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EVOLUTIONARY APPLICATIONS 2014年第3期7卷 403-412页

作者： Aris-Brosou, Stephane Univ Ottawa Dept Biol Ctr Adv Res Environm Genom Ottawa ON K1N 6N5 Canada Univ Ottawa Dept Math & Stat Ottawa ON K1N 6N5 Canada

Influenza is one of the most severe respiratory infections affecting humans throughout the world, yet the dynamics of its global transmission network are still contentious. Here, I describe a novel combination of phylogenetics, time series, and graph theory to analyze 14.25years of data stratified in space and in time, focusing on the main target of the human immune response, the hemagglutinin gene. While bypassing the complete phylogenetic inference of huge data sets, the method still extracts information suggesting that waves of genetic or of nucleotide diversity circulate continuously around the globe for subtypes that undergo sustained transmission over several seasons, such as H3N2 and pandemic H1N1/09, while diversity of prepandemic H1N1 viruses had until 2009 a noncontinuous transmission pattern consistent with a source/sink model. Irrespective of the shift in the structure of H1N1 diversity circulation with the emergence of the pandemic H1N1/09 strain, US prevalence peaks during the winter months when genetic diversity is at its lowest. This suggests that a dominant strain is generally responsible for epidemics and that monitoring genetic and/or nucleotide diversity in real time could provide public health agencies with an indirect estimate of prevalence.

关键词： genetic diversity global dynamics influenza pagerank algorithm prevalence transmission network

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Computing the pagerank Variation for Fragile Web Data

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SICE Journal of Control, Measurement, and System Integration 2009年第1期2卷 1-9页

作者： Hideaki Ishii Roberto Tempo Department of Computational Intelligence &ampSystems Science Tokyo Institute of Technology IEIIT-CNR Politecnico di Torino

Search engines utilize numerous measures to rank the webpages in the search results. At Google, the so-called pagerank algorithm provides crucial information by quantifying the importance of webpages. The pagerank values are considered to be objective since the algorithm requires only the data of the web link structure. In this paper, we first introduce the original pagerank algorithm and give a brief overview of the distributed randomized approach recently proposed by the authors. Then, we consider the problem of computing the variation in the pagerank values that can be caused by erroneous data in the web structure when some links are fragile. An efficient (centralized) algorithm in the form of linear programming is proposed. This approach employs results from the field of interval matrices. Numerical examples are given to verify the effectiveness of the approach.

关键词： distributed computation interval matrices multi-agent systems pagerank algorithm randomized algorithms search engines

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An effective fault localization approach based on pagerank and mutation analysis

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JOURNAL OF SYSTEMS AND SOFTWARE 2023年第1期204卷

作者： Yan, Yue Jiang, Shujuan Zhang, Yanmei Zhang, Cheng China Univ Min & Technol Engn Res Ctr Mine Digitalizat Minist Educ Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China

Mutation-based fault localization (MBFL) is a popular method based on mutation testing. MBFL applies a variety of operators to generate mutants and calculates the statement's suspiciousness by counting the execution results of the test cases on the mutants. However, the tie problem of MBFL creates obstacles to accurate fault localization. The tie problem refers to that many statements have the same suspiciousness. To solve the tie problem, we propose a fault localization approach based on the pagerank algorithm and mutation analysis (PRMA). We first apply the pagerank algorithm to calculate the faultiness scores of the statements. Then, we weight the suspicious value of the statements with faultiness scores to solve the tie problem. Finally, the weighted suspicious values are sorted in descending order to generate a list, which is provided to developers for fault localization. To evaluate our approach, we conduct experiments on the real fault benchmark Defects4J and the artificial fault dataset Siemens. We compare PRMA with the traditional MBFL techniques (Metallaxis and MUSE) and recently proposed MBFL methods (MCBFL-hybrid-avg, SMFL and SMBFL). The experimental results show that our approach outperforms above comparison methods and improves the effect of fault localization in both quantity and accuracy.& COPY;2023 Elsevier Inc. All rights reserved.

关键词： Software fault localization Mutation-based fault localization pagerank algorithm Mutation analysis

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A pagerank-BASED COLLABORATIVE FILTERING RECOMMENDATION APPROACH IN DIGITAL LIBRARIES

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2017年第4期24卷 1051-1058页

作者： Guo, Shanshan Zhang, Wenyu Zhang, Shuai Zhejiang Univ Finance & Econ Lib 18 Xueyuan St Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Finance & Econ Sch Informat 18 Xueyuan St Hangzhou 310018 Zhejiang Peoples R China

In the current era of big data, the explosive growth of digital resources in Digital Libraries (DLs) has led to the serious information overload problem. This trend demands personalized recommendation approaches to provide DL users with digital resources specific to their individual needs. In this paper we present a personalized digital resource recommendation approach, which combines pagerank and Collaborative Filtering (CF) techniques in a unified framework for recommending right digital resources to an active user by generating and analyzing a time-aware network of both user relationships and resource relationships from historical usage data. To address the existing issues in DL deployment, including unstable user profiles, unstable digital resource features, data sparsity and cold start problem, this work adapts the personalized pagerank algorithm to rank the time-aware resource importance for more effective CF, by searching for associative links connecting both active user and his/her initially preferred resources. We further evaluate the performance of the proposed methodology through a case study relative to the traditional CF technique operating on the same historical usage data from a DL.

关键词： collaborative filtering digital library pagerank algorithm recommendation approach social network

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pagerank-based Traffic Simulation Using Taxi Probe Data

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Procedia Computer Science 2013年 22卷 1156-1163页

作者： Naoto Mukai Department of Culture-Information Studies School of Culture-Information Studies Sugiyama Jogakuen University 17-3 Hoshigaoka-motomachi Chikusa-ku Nagoya Aichi 464-8662 Japan

Traffic analysis is an important work to transport people and goods to their destinations in a quick and efficient manner. In these years, probe taxis, which equip with some sensors (e.g., global positioning system), become widely used in Tokyo. Additionally, their sensor data enable us to predict the amount of future traffic. In this paper, we report a web-based viewer of taxi probe data which are used for traffic simulation. Moreover, we propose a traffic simulation model based on the concept of “pagerank”, which is used for the Google search engine, to gain a quick overview of traffic flow in Tokyo. The original algorithm of “pagerank” needs a matrix calculation called “Google matrix”, but the matrix calculation can be simulated by transitions of web surfers among web pages. Thus, we also assume the transitions of taxis as the same characteristics of the web surfers, and develop a traffic simulator to find important spots which have a strong effect on transportation efficiency.

关键词： Traffic Simulation Model Taxi Probe Data pagerank algorithm

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