Georgios A Pavlopoulos, David Paez-Espino, Nikos C Kyrpides, Ioannis Iliopoulos
{"title":"大规模网络分析可视化工具的实证比较。","authors":"Georgios A Pavlopoulos, David Paez-Espino, Nikos C Kyrpides, Ioannis Iliopoulos","doi":"10.1155/2017/1278932","DOIUrl":null,"url":null,"abstract":"<p><p>Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today's indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. We comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities.</p>","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2017 ","pages":"1278932"},"PeriodicalIF":0.0000,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2017/1278932","citationCount":"50","resultStr":"{\"title\":\"Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis.\",\"authors\":\"Georgios A Pavlopoulos, David Paez-Espino, Nikos C Kyrpides, Ioannis Iliopoulos\",\"doi\":\"10.1155/2017/1278932\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today's indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. We comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities.</p>\",\"PeriodicalId\":39059,\"journal\":{\"name\":\"Advances in Bioinformatics\",\"volume\":\"2017 \",\"pages\":\"1278932\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1155/2017/1278932\",\"citationCount\":\"50\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2017/1278932\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2017/7/18 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"Biochemistry, Genetics and Molecular Biology\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2017/1278932","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/7/18 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis.
Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today's indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. We comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities.