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Disease-Pathway Association Prediction Based on Random Walks With Restart and pagerank

IEEE ACCESS 2020年 8卷 72021-72038页

作者： Ghulam, Ali Lei, Xiujuan Guo, Min Bian, Chen Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

The study of disease-pathway association in human diseases is a perennial focus of the biomedical field. The association of diseases and pathways can help in the discovery of the mechanisms or relationships of human diseases. The accuracy of disease identification has been less than satisfactory despite decades of research in this area. Therefore, this study proposes a computational model for the prediction of disease-pathway associations. The proposed computational model is based on Random Walk with Restart on heterogeneous network (RWRH) and pagerank. The RWRH disease-pathway association model is a novel computational model that can predict potential disease-pathway associations. Furthermore, the model can help pathologists understand the correlations among disease-pathway associations, treatments, and reactions. We performed a pathway-based study to expand disease variation relationships and to find new molecular correlations between genetic mutations. We constructed a biological network on the basis of shared gene interactions of disease-pathways and attempted to investigate the pathogenesis of a disease by analyzing the constructed network. The network construction was based on two parts. First, the similarity between pathway-pathway networks was calculated. Second, a disease-disease (DD) similarity network was constructed, and the correlation between disease and disease similarity was calculated. We also investigated the pathway seed node and disease seed node with high pagerank. Moreover, we focused on mining the complexity of disease-pathway associations. We used the bipartite network of disease-pathway associations to combine the obtained biological information, which was based on the pair similarity of sequence expression weights. These weights, which were obtained by using the multilayer resource-allocation algorithm, were used to calculate the prediction scores of each disease-pathway pair. Here, through leave-one-out cross-validation, we examined a 210

关键词： Pathway similarity network disease similarity network disease pathway association pagerank algorithm

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A pagerank-based reputation model for personalised manufacturing service recommendation

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ENTERPRISE INFORMATION SYSTEMS 2017年第5期11卷 672-693页

作者： Zhang, W. Y. Zhang, S. Guo, S. S. Zhejiang Univ Finance & Econ Sch Informat Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Finance & Econ Lib Hangzhou 310018 Zhejiang Peoples R China

The number of manufacturing services for cross-enterprise business collaborations is increasing rapidly because of the explosive growth of Web service technologies. This trend demands intelligent and robust models to address information overload in order to enable efficient discovery of manufacturing services. In this paper, we present a personalised manufacturing service recommendation approach, which combines a pagerank-based reputation model and a collaborative filtering technique in a unified framework for recommending the right manufacturing services to an active service user for supply chain deployment. The novel aspect of this research is adapting the pagerank algorithm to a network of service-oriented multi-echelon supply chain in order to determine both user reputation and service reputation. In addition, it explores the use of these methods in alleviating data sparsity and cold start problems that hinder traditional collaborative filtering techniques. A case study is conducted to validate the practicality and effectiveness of the proposed approach in recommending the right manufacturing services to active service users.

关键词： Collaborative filtering manufacturing service pagerank algorithm service discovery service recommendation social network

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Using pagerank in the analysis of technological progress through patents: an illustration for biotechnological inventions

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SCIENTOMETRICS 2017年第3期113卷 1407-1438页

作者： Reinstaller, Andreas Reschenhofer, Peter Austrian Inst Econ Res WIFO Arsenal Obj 20 A-1030 Vienna Austria

This paper examines whether pagerank algorithms are a valid instrument for the analysis of technical progress in specific technological fields by means of patent citation data. It provides evidence for patent data in biotechnology. Recent literature has been critical with regard to the use of pagerank for the analysis of scientific citation networks. The results reported in this paper indicate, however, that with some minor adaptations and careful interpretation of the results the algorithm can be used to capture some important stylised facts of technical progress and the importance of single patents relatively well especially if compared to indicators based on direct inward citations only.

关键词： Patent citations Technological progress pagerank algorithm Biotechnology

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Match-Ignition: Plugging pagerank into Transformer for Long-form Text Matching 21

Match-Ignition: Plugging PageRank into Transformer for Long-...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Pang, Liang Lan, Yanyan Cheng, Xueqi Chinese Acad Sci Inst Comp Technol Data Intelligence Syst Res Ctr Beijing Peoples R China Tsinghua Univ Inst AI Ind Res Beijing Peoples R China Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781450384469

Neural text matching models have been widely used in community question answering, information retrieval, and dialogue. However, these models designed for short texts cannot well address the long-form text matching problem, because there are many contexts in long-form texts can not be directly aligned with each other, and it is difficult for existing models to capture the key matching signals from such noisy data. Besides, these models are computationally expensive for simply use all textual data indiscriminately. To tackle the effectiveness and efficiency problem, we propose a novel hierarchical noise filtering model, namely Match-Ignition. The main idea is to plug the well-known pagerank algorithm into the Transformer, to identify and filter both sentence and word level noisy information in the matching process. Noisy sentences are usually easy to detect because previous work has shown that their similarity can be explicitly evaluated by the word overlapping, so we directly use pagerank to filter such information based on a sentence similarity graph. Unlike sentences, words rely on their contexts to express concrete meanings, so we propose to jointly learn the filtering and matching process, to well capture the critical word-level matching signals. Specifically, a word graph is first built based on the attention scores in each self-attention block of Transformer, and key words are then selected by applying pagerank on this graph. In this way, noisy words will be filtered out layer by layer in the matching process. Experimental results show that Match-Ignition outperforms both SOTA short text matching models and recent long-form text matching models. We also conduct detailed analysis to show that Match-Ignition efficiently captures important sentences and words, to facilitate the long-form text matching process.

关键词： Text Matching Long-form Text pagerank algorithm

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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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An application of Google’s pagerank to NFL rankings

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Involve 2012年第4期5卷 463-471页

作者： Zack, Laurie Lamb, Ron Ball, Sarah High Point University 833 Montlieu Avenue High Point 27262 NC United States

We explain the pagerank algorithm and its application to the ranking of football teams via the GEM method. We then modify and extend the GEM method with the addition of more football statistics to look at the possibility of using this method to more accurately rank teams. Lastly, we compare both methods by aggregating each statistical ranking using the cross-entropy Monte Carlo algorithm. © 2012 Mathematical Sciences Publishers.

关键词： linear algebra pagerank algorithm ranking football teams

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A Distributed Randomized Approach for the pagerank Computation: Part 2

A Distributed Randomized Approach for the PageRank Computati...

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47th IEEE Conference on Decision and Control

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

ISBN: (纸本)9781424431243

In the search engine of Google, the pagerank algorithm plays a crucial role in ranking the obtained results. The algorithm determines the importance of each web page based on the link structure of the web. In this two-part paper, we propose a distributed randomized approach for the pagerank computation, where the pages locally update their values by communicating with linked pages. This paper is the second part, and we develop two enhanced distributed schemes which deal with simultaneous updates and update termination of the computations, respectively.

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

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A Distributed Randomized Approach for the pagerank Computation: Part 1

A Distributed Randomized Approach for the PageRank Computati...

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47th IEEE Conference on Decision and Control

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

ISBN: (纸本)9781424431243

In the search engine of Google, the pagerank algorithm plays a crucial role in ranking the obtained results. The algorithm quantifies the importance of each web page based on the link structure of the web. In this two-part paper, 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. 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 Stochastic matrices

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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 pagerank Computation with Link Failures

Distributed PageRank Computation with Link Failures

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American Control Conference 2009

作者： Ishii, Hideaki Tempo, Roberto Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku 4259-J2-54Nagatsuta Cho Yokohama Kanagawa 2268502 Japan Politecn Torino CNR IEIIT I-10129 Turin Italy

ISBN: (纸本)9781424445233

The Google search engine employs the so-called pagerank algorithm for ranking the search results. This algorithm quantifies the importance of each web page based on the link structure of the web. In this paper, we continue our recent work on distributed randomized computation of pagerank, where the pages locally determine their values by communicating with linked pages. In particular, we propose a distributed randomized algorithm with limited information, where only part of the linked pages is required to be contacted. This is useful to enhance flexibility and robustness in computation and communication.

关键词： Distributed computation Link failures Multiagent consensus pagerank algorithm Randomization Stochastic matrices

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