CiteULike | Further Information

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Mendeley | Further Information

{"title"=>"Hypergraphs and cellular networks", "type"=>"generic", "authors"=>[{"first_name"=>"Steffen", "last_name"=>"Klamt", "scopus_author_id"=>"6603425714"}, {"first_name"=>"Utz Uwe", "last_name"=>"Haus", "scopus_author_id"=>"12143386200"}, {"first_name"=>"Fabian", "last_name"=>"Theis", "scopus_author_id"=>"6701364934"}], "year"=>2009, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"67049098922", "issn"=>"1553734X", "doi"=>"10.1371/journal.pcbi.1000385", "pmid"=>"19478865", "pui"=>"354725636", "scopus"=>"2-s2.0-67049098922", "isbn"=>"1553-7358 (Electronic)"}, "id"=>"dd872ffc-e5b0-3e7f-87cf-a7c1058b7907", "abstract"=>"Web of Science", "link"=>"http://www.mendeley.com/research/hypergraphs-cellular-networks", "reader_count"=>275, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>20, "Librarian"=>1, "Student > Doctoral Student"=>12, "Researcher"=>72, "Student > Ph. D. Student"=>92, "Student > Postgraduate"=>11, "Student > Master"=>22, "Other"=>7, "Student > Bachelor"=>11, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>3, "Professor"=>16}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>20, "Librarian"=>1, "Student > Doctoral Student"=>12, "Researcher"=>72, "Student > Ph. D. Student"=>92, "Student > Postgraduate"=>11, "Student > Master"=>22, "Other"=>7, "Student > Bachelor"=>11, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>3, "Professor"=>16}, "reader_count_by_subject_area"=>{"Unspecified"=>15, "Agricultural and Biological Sciences"=>102, "Arts and Humanities"=>3, "Philosophy"=>1, "Business, Management and Accounting"=>1, "Chemical Engineering"=>2, "Chemistry"=>2, "Computer Science"=>66, "Earth and Planetary Sciences"=>2, "Economics, Econometrics and Finance"=>1, "Engineering"=>18, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>15, "Mathematics"=>12, "Medicine and Dentistry"=>3, "Neuroscience"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Physics and Astronomy"=>21, "Psychology"=>3, "Social Sciences"=>2, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Social Sciences"=>{"Social Sciences"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>21}, "Psychology"=>{"Psychology"=>3}, "Mathematics"=>{"Mathematics"=>12}, "Unspecified"=>{"Unspecified"=>15}, "Environmental Science"=>{"Environmental Science"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Chemical Engineering"=>{"Chemical Engineering"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>3}, "Engineering"=>{"Engineering"=>18}, "Chemistry"=>{"Chemistry"=>2}, "Neuroscience"=>{"Neuroscience"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>102}, "Computer Science"=>{"Computer Science"=>66}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>15}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Colombia"=>3, "United States"=>14, "Japan"=>1, "United Kingdom"=>7, "Portugal"=>4, "Switzerland"=>1, "India"=>4, "Russia"=>2, "Spain"=>1, "Netherlands"=>2, "South Korea"=>1, "Sweden"=>1, "Austria"=>1, "Iran"=>3, "China"=>1, "Finland"=>1, "South Africa"=>1, "Italy"=>1, "Slovenia"=>1, "France"=>2, "Germany"=>7}, "group_count"=>15}

ORCID | Further Information

CrossRef

• http://doi.org/10.1016/j.biosystems.2011.06.002
• http://doi.org/10.1109/TCBB.2010.117
• http://doi.org/10.1016/j.physrep.2012.02.006
• http://doi.org/10.1007/s10955-018-2026-9
• http://doi.org/10.1016/j.physrep.2020.05.004
• http://doi.org/10.3390/systems5010023
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• http://doi.org/10.1016/j.biotechadv.2019.04.001
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• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/25d72994-2889-42d0-bed1-083af7f54c02", "title"=>"Another Perspective article on Meta- and Hypergraphs for Biological Networks", "body"=>"Hu Z, Mellor J, Wu J, Kanehisa M, Stuart JM, DeLisi C (2007)\r\nTowards zoomable multidimensional maps of the cell.\r\nNat Biotechnol 25(5): 547-554. \r\nhttp://dx.doi.org/10.1038/nbt1304\r\n", "isRemoved"=>false, "created"=>"2009-06-04T18:36:47Z", "lastModified"=>"2009-06-04T18:36:47Z", "creator"=>{"userId"=>"161605"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pcbi.1000385", "state"=>"published", "journals"=>{"PLoSCompBiol"=>{"journalKey"=>"PLoSCompBiol", "eIssn"=>"1553-7358", "title"=>"PLOS Computational Biology"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2009-06-04T18:36:47Z", "replies"=>[]}
• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/9a34a92d-0ca5-4ad0-9a5f-aeee3894770a", "title"=>"hypergraphs are also useful for sequence alignment!", "body"=>"I enjoyed your Perspective on hypergraphs for cellular biology. We published an article, \"Fast Statistical Alignment,\" in this same issue where we use hypergraphs for a rather different application. We explicitly represent a multiple sequence alignment as a hypergraph (which can be described by a partially ordered set) and then dynamically construct the hypergraph using pairwise homology statements.\n", "isRemoved"=>false, "created"=>"2009-06-08T16:16:56Z", "lastModified"=>"2009-06-08T16:16:56Z", "creator"=>{"userId"=>"117825"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pcbi.1000385", "state"=>"published", "journals"=>{"PLoSCompBiol"=>{"journalKey"=>"PLoSCompBiol", "eIssn"=>"1553-7358", "title"=>"PLOS Computational Biology"}}}, "replyTreeSize"=>1, "mostRecentActivity"=>"2009-06-09T09:33:38Z", "replies"=>[{"type"=>"REPLY", "parentID"=>13957, "annotationUri"=>"info:doi/10.1371/reply/23e1b91e-3447-400a-a842-4ba0b2e1bf1f", "title"=>"RE: hypergraphs are also useful for sequence alignment!", "body"=>"You can find the article \"Fast Statistical Alignment\" here:\nhttp://www.ploscompbiol.org/doi/pcbi.1000392 ", "isRemoved"=>false, "created"=>"2009-06-09T09:33:38Z", "lastModified"=>"2009-06-09T09:33:38Z", "creator"=>{"userId"=>"40493"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pcbi.1000385", "state"=>"published", "journals"=>{"PLoSCompBiol"=>{"journalKey"=>"PLoSCompBiol", "eIssn"=>"1553-7358", "title"=>"PLOS Computational Biology"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2009-06-09T09:33:38Z", "replies"=>[]}]}
• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/8c00d5a7-958c-44fe-b3da-98c49df10b5e", "title"=>"Hypergraphs are also useful utilizing prior knowleges with multiple types of data integration", "body"=>"Thanks for an interesting perspective on hypergraphs.\nWe published a hypergraph-based learning algorithm to integrate gene expression and protein-protein interaction data for cancer outcome prediction.\nAnd more comprehensive hypergraph-based learning algorithm paper recently accepted by Bioinformatic journal.\n\nYou can find the first conference paper: \"Learning on Weighted Hypergraphs to Integrate Protein Interactions and Gene Expressions for Cancer Outcome Prediction\"\n\nhttp://compbio.cs.umn.edu/Hyper_Gene/\n\nThe journal paper will be posted soon.", "isRemoved"=>false, "created"=>"2009-07-29T15:38:00Z", "lastModified"=>"2009-07-29T15:38:00Z", "creator"=>{"userId"=>"133565"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pcbi.1000385", "state"=>"published", "journals"=>{"PLoSCompBiol"=>{"journalKey"=>"PLoSCompBiol", "eIssn"=>"1553-7358", "title"=>"PLOS Computational Biology"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2009-07-29T15:38:00Z", "replies"=>[]}

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Figshare

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• {"files"=>["https://ndownloader.figshare.com/files/896312"], "description"=>"<p>Choose two distinct hyperedges and two different vertices contained in either\n of the two. Then swap them. Clearly this operation keeps both degree\n distributions fixed. After a certain number of iterations, the\n thus-generated Markov chain produces independent samples of the underlying\n random hypergraph with given degree distributions. In the figure, this is\n illustrated using the in-this-case simpler-to-visualize bipartite version.\n The gray double-arrows indicate edges to be swapped. Each of the three\n swaps, (A,H₂)–(C,H₃),\n (B,H₁)–(E,H₃), and\n (B,H₃)–(D,H₁), does not change the\n vertex and edge degrees. Significance analysis of the CORUM protein complex\n hypergraph was done in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000385#pcbi.1000385-Wong1\" target=\"_blank\">[44]</a> using this idea.</p>", "links"=>[], "tags"=>["hypergraph", "null"], "article_id"=>566774, "categories"=>["Computational Biology", "Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Steffen Klamt", "Utz-Uwe Haus", "Fabian Theis"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000385.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Generating_a_hypergraph_null_model_by_rewiring_/566774", "title"=>"Generating a hypergraph null model by rewiring.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 05:47:38"}

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• {"unique-ip"=>"35", "full-text"=>"37", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
• {"unique-ip"=>"31", "full-text"=>"37", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
• {"unique-ip"=>"53", "full-text"=>"74", "pdf"=>"19", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"6", "year"=>"2020", "month"=>"2"}
• {"unique-ip"=>"50", "full-text"=>"67", "pdf"=>"12", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2020", "month"=>"3"}
• {"unique-ip"=>"55", "full-text"=>"73", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2020", "month"=>"4"}
• {"unique-ip"=>"40", "full-text"=>"54", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2020", "month"=>"5"}

Relative Metric

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F1000Prime