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Mendeley | Further Information

{"title"=>"Network archaeology: Uncovering ancient networks from present-day interactions", "type"=>"journal", "authors"=>[{"first_name"=>"Saket", "last_name"=>"Navlakha", "scopus_author_id"=>"23486588700"}, {"first_name"=>"Carl", "last_name"=>"Kingsford", "scopus_author_id"=>"35611336100"}], "year"=>2011, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"79955554976", "doi"=>"10.1371/journal.pcbi.1001119", "pui"=>"361692973", "arxiv"=>"1008.5166", "issn"=>"1553734X", "pmid"=>"21533211", "isbn"=>"1553-7358", "scopus"=>"2-s2.0-79955554976"}, "id"=>"ee5b9a52-b508-391b-b34e-7b2bece1bbbf", "abstract"=>"What proteins interacted in a long-extinct ancestor of yeast? How have different members of a protein complex assembled together over time? Our ability to answer such questions has been limited by the unavailability of ancestral protein-protein interaction (PPI) networks. To overcome this limitation, we propose several novel algorithms to reconstruct the growth history of a present-day network. Our likelihood-based method finds a probable previous state of the graph by applying an assumed growth model backwards in time. This approach retains node identities so that the history of individual nodes can be tracked. Using this methodology, we estimate protein ages in the yeast PPI network that are in good agreement with sequence-based estimates of age and with structural features of protein complexes. Further, by comparing the quality of the inferred histories for several different growth models (duplication-mutation with complementarity, forest fire, and preferential attachment), we provide additional evidence that a duplication-based model captures many features of PPI network growth better than models designed to mimic social network growth. From the reconstructed history, we model the arrival time of extant and ancestral interactions and predict that complexes have significantly re-wired over time and that new edges tend to form within existing complexes. We also hypothesize a distribution of per-protein duplication rates, track the change of the network's clustering coefficient, and predict paralogous relationships between extant proteins that are likely to be complementary to the relationships inferred using sequence alone. Finally, we infer plausible parameters for the model, thereby predicting the relative probability of various evolutionary events. The success of these algorithms indicates that parts of the history of the yeast PPI are encoded in its present-day form.", "link"=>"http://www.mendeley.com/research/network-archaeology-uncovering-ancient-networks-presentday-interactions", "reader_count"=>142, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>10, "Researcher"=>41, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>43, "Student > Postgraduate"=>4, "Student > Master"=>15, "Other"=>2, "Student > Bachelor"=>8, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>10}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>10, "Researcher"=>41, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>43, "Student > Postgraduate"=>4, "Student > Master"=>15, "Other"=>2, "Student > Bachelor"=>8, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>10}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Agricultural and Biological Sciences"=>70, "Arts and Humanities"=>5, "Computer Science"=>26, "Economics, Econometrics and Finance"=>1, "Engineering"=>3, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>5, "Mathematics"=>5, "Medicine and Dentistry"=>1, "Neuroscience"=>1, "Physics and Astronomy"=>5, "Psychology"=>3, "Social Sciences"=>8, "Immunology and Microbiology"=>1, "Linguistics"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>8}, "Physics and Astronomy"=>{"Physics and Astronomy"=>5}, "Psychology"=>{"Psychology"=>3}, "Mathematics"=>{"Mathematics"=>5}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>5}, "Engineering"=>{"Engineering"=>3}, "Neuroscience"=>{"Neuroscience"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>70}, "Computer Science"=>{"Computer Science"=>26}, "Linguistics"=>{"Linguistics"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}}, "reader_count_by_country"=>{"Colombia"=>1, "United States"=>11, "Japan"=>2, "United Kingdom"=>8, "Spain"=>4, "India"=>2, "Russia"=>1, "Greece"=>1, "Canada"=>1, "Netherlands"=>1, "Belgium"=>2, "Luxembourg"=>2, "Brazil"=>3, "Italy"=>1, "Mexico"=>1, "Slovenia"=>1, "Australia"=>1, "France"=>2, "Chile"=>1, "Germany"=>3}, "group_count"=>5}

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• http://doi.org/10.1186/1748-7188-7-25
• http://doi.org/10.1016/j.pharmthera.2013.01.016
• http://doi.org/10.1093/bib/bbs072
• http://doi.org/10.1371/journal.pone.0058134
• http://doi.org/10.1038/s41598-019-38912-0
• http://doi.org/10.1111/rssb.12289
• http://doi.org/10.1098/rsif.2014.0283
• http://doi.org/10.1093/bioinformatics/bts211
• http://doi.org/10.1186/s12859-016-1395-9
• http://doi.org/10.1128/MMBR.05021-11
• http://doi.org/10.1007/s10618-014-0399-1
• http://doi.org/10.1103/PhysRevE.86.041919
• http://doi.org/10.1038/s41598-017-05686-2
• http://doi.org/10.1109/TCBB.2013.14
• http://doi.org/10.1162/neco_a_01095
• http://doi.org/10.1039/C1MB05205E
• http://doi.org/10.1371/journal.pcbi.1004347
• http://doi.org/10.1016/j.pbi.2012.01.003
• http://doi.org/10.1038/nrg3033
• http://doi.org/10.1016/j.febslet.2012.02.040
• http://doi.org/10.1089/cmb.2012.0295
• http://doi.org/10.1093/bioinformatics/btt224
• http://doi.org/10.1162/NECO_a_00924
• http://doi.org/10.1016/j.jtbi.2013.05.006
• http://doi.org/10.1186/s12862-016-0731-z
• http://doi.org/10.1109/TNB.2017.2656058
• http://doi.org/10.1534/g3.113.008458
• http://doi.org/10.1109/ACCESS.2017.2690664

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• 3372256
• 3647006
• 4517947
• 4032542
• 3492119
• 3418285
• 3439388
• 3603955
• 3371843
• 3694678
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• 3815064

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• {"id"=>"225286855539703808", "text"=>"#PP54 #ISMB Network Archaeology: Uncovering Ancient Networks from Present-Day Interactions paper is here: http://t.co/vUghxL2x", "created_at"=>"2012-07-17T17:52:26Z", "user"=>"iddux", "user_name"=>"Iddo Friedberg", "user_profile_image"=>"http://a0.twimg.com/profile_images/1668503928/qr-20111201045635_normal.png"}
• {"id"=>"312548178476007426", "text"=>"@KosukeShinoda たぶんこれです。今のネットワークの姿から過去の姿を復元するというもの。 http://t.co/X0vtZvt5Zl", "created_at"=>"2013-03-15T12:57:47Z", "user"=>"toyodam", "user_name"=>"Masashi Toyoda 豊田正史", "user_profile_image"=>"http://a0.twimg.com/profile_images/1434136716/toyoda-face-b_normal.jpg"}
• {"id"=>"312574358411427841", "text"=>"RT @toyodam: @KosukeShinoda たぶんこれです。今のネットワークの姿から過去の姿を復元するというもの。 http://t.co/X0vtZvt5Zl", "created_at"=>"2013-03-15T14:41:49Z", "user"=>"toritorix", "user_name"=>"tori　tori", "user_profile_image"=>"http://a0.twimg.com/profile_images/2463123024/5xx4eiba232d25rv23qs_normal.jpeg"}
• {"id"=>"391323639728992256", "text"=>"@alanyliu cites discussion of network archeology http://t.co/xVx73PwPYQ", "created_at"=>"2013-10-18T22:03:20Z", "user"=>"Prof_Clinty", "user_name"=>"Clint Burnham", "user_profile_image"=>"http://pbs.twimg.com/profile_images/3240794297/bf7d8c2957c367167b8372e009b469cf_normal.jpeg"}
• {"id"=>"915957251449397248", "text"=>"Network Archaeology: Uncovering Ancient Networks from Present-Day Interaction https://t.co/pm00o4mPdz Navlakha et a… https://t.co/o5nyjdcSwL", "created_at"=>"2017-10-05T15:10:01Z", "user"=>"PLOSCompBiol", "user_name"=>"PLOS Comp Biol", "user_profile_image"=>"http://pbs.twimg.com/profile_images/2435436417/hg8nh61i55aibgx0hcvp_normal.jpeg"}

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• {"month"=>"1", "year"=>"2018", "pdf_views"=>"11", "xml_views"=>"0", "html_views"=>"32"}
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• {"month"=>"8", "year"=>"2018", "pdf_views"=>"44", "xml_views"=>"2", "html_views"=>"26"}
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• {"month"=>"11", "year"=>"2018", "pdf_views"=>"18", "xml_views"=>"1", "html_views"=>"17"}
• {"month"=>"12", "year"=>"2018", "pdf_views"=>"15", "xml_views"=>"0", "html_views"=>"21"}
• {"month"=>"1", "year"=>"2019", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"22"}
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Figshare

• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783281/Figure_1.tif"], "description"=>"<p>(A) The probabilities governing the DMC model. (B) An example iteration of the DMC model.</p>", "links"=>[], "tags"=>["complementarity"], "article_id"=>453666, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Duplication_mutation_with_complementarity_DMC_/453666", "title"=>"Duplication-mutation with complementarity (DMC).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:10:27"}
• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783359/Figure_2.tif"], "description"=>"<p>Pseudocode for reversing a network using the DMC model.</p>", "links"=>[], "tags"=>["reversing", "dmc"], "article_id"=>453735, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Pseudocode_for_reversing_a_network_using_the_DMC_model_/453735", "title"=>"Pseudocode for reversing a network using the DMC model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:10:54"}
• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783582/Figure_3.tif"], "description"=>"<p>The - and -axes show the DMC parameters () used to grow the synthetic network forward. Each parameter varies from 0.1–0.9 in steps of 0.2. The intensity of each cell in the heatmap represents the quality of the reconstruction validation measure (Anchor, Kendall, Footrule) under optimal reverse parameters. (A) and (B) show results under varying levels of noise. Error bars (not shown) indicate a standard deviation of roughly 7–8% for Kendall and 4–5% for Footrule (over 1000 trials). For many DMC-grown synthetic networks, accurate reconstruction is possible.</p>", "links"=>[], "tags"=>["node", "times", "anchors", "dmc"], "article_id"=>453956, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Accuracy_of_node_arrival_times_and_node_anchors_using_the_DMC_model_/453956", "title"=>"Accuracy of node arrival times and node anchors using the DMC model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:12:03"}
• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783701/Figure_4.tif"], "description"=>"<p>Synthetic DMC-grown networks were constructed using and reversed using all 25 combinations of reversal parameters. The - and -axes show the difference between the reversal parameters ( and , respectively) and the forward parameters (0.1 and 0.9, respectively). The intensity of each cell in the heatmap represents the quality of the reconstruction validation measure with standard deviation lying between 1–7% for Anchor, 7–8% for Kendall, and 4–5% for Footrule. Accurate histories can be inferred as long as reverse parameters (in particular, ) are in the rough range of the forward parameters.</p>", "links"=>[], "tags"=>["node", "times", "anchors", "parameters"], "article_id"=>454074, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Accuracy_of_node_arrival_times_and_node_anchors_when_reverse_parameters_are_not_known_/454074", "title"=>"Accuracy of node arrival times and node anchors when reverse parameters are not known.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:12:40"}
• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783787/Figure_5.tif"], "description"=>"<p>(A–D) The -axis shows the FF parameter () used to grow the synthetic network forward. (Values of parameter resulted in mostly clique-like networks.) The -axis shows the quality of the 3 reconstruction validation measures under optimal reverse parameters (bars show standard deviation over 1000 trials). All FF-based reconstructions are significantly better than random reconstructions, even when 30% of true edges are replaced by random edges.</p>", "links"=>[], "tags"=>["times", "node", "anchors"], "article_id"=>454167, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Accuracy_of_arrival_times_and_node_anchors_using_the_forest_fire_model_/454167", "title"=>"Accuracy of arrival times and node anchors using the forest fire model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:13:07"}
• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/783927/Figure_6.tif"], "description"=>"<p>(A–D) The -axis shows the PA parameter () used to grow the synthetic network forward. The -axis shows the quality of the 3 reconstruction validation measures (bars show standard deviation over 1000 trials). Compared to the DMC and FF models, the PA model is easiest to reverse, even in the presence of substantial noise.</p>", "links"=>[], "tags"=>["times", "preferential", "attachment"], "article_id"=>454305, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Agreement_with_arrival_times_using_the_preferential_attachment_model_/454305", "title"=>"Agreement with arrival times using the preferential attachment model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:13:51"}
• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/784089/Figure_7.tif"], "description"=>"<p>(A) Visualization <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1001119#pcbi.1001119-Shannon1\" target=\"_blank\">[59]</a> of the extant PPI network () and an ancestral version (). The density, clustering coefficient (CC), average shortest path length (SPL), and average -core number are shown for each network. The ancient network is considerably denser than the extant network. (B) The change in clustering coefficient over time in the yeast network history. Recent evolution (after time step 2000) reveals a trend of decreasing modularity, perhaps due to the addition of peripheral units to existing complexes or pathways. Older evolution (prior to time step 2000, excluding the initial effect of small networks) shows an increasing modularity, suggesting that new clusters were emerging. Other methods have found evidence for both increasing <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1001119#pcbi.1001119-Gibson1\" target=\"_blank\">[26]</a> and decreasing <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1001119#pcbi.1001119-Vazquez1\" target=\"_blank\">[8]</a> clustering coefficient over time.</p>", "links"=>[], "tags"=>["extant"], "article_id"=>454464, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Comparing_extant_and_ancient_networks_/454464", "title"=>"Comparing extant and ancient networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:14:34"}
• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/784219/Figure_8.tif"], "description"=>"<p>The -axis shows the class-based Kendall value of the predicted ordering. The DMC model more accurately orders the proteins in the classes compared to FF and PA.</p>", "links"=>[], "tags"=>["groups", "reversing", "dmc", "ff", "models", "ppi"], "article_id"=>454592, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.g008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Predicting_protein_age_groups_by_reversing_the_DMC_and_FF_models_on_a_real_PPI_network_for_S_cerevisiae_/454592", "title"=>"Predicting protein age groups by reversing the DMC and FF models on a real PPI network for <i>S. cerevisiae</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:15:15"}
• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/784331/Figure_9.tif"], "description"=>"<p>Visualization of the node/anchor phylogeny inferred by reversing the DMC model on the yeast PPI network.</p>", "links"=>[], "tags"=>["phylogeny", "inferred", "reversing", "dmc", "yeast", "ppi"], "article_id"=>454702, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.g009"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Visualization_of_the_node_anchor_phylogeny_inferred_by_reversing_the_DMC_model_on_the_yeast_PPI_network_/454702", "title"=>"Visualization of the node/anchor phylogeny inferred by reversing the DMC model on the yeast PPI network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:15:53"}
• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/784497/Figure_10.tif"], "description"=>"<p>(A) The distribution of duplication rates for extant proteins in the PPI network. The -axis of the histogram is the number of duplications, measured as the distance from the root of the phylogeny to the extant protein. The -axis is the percentage of proteins lying in the tree depth bin. (B) The relationship between duplication and number of interaction partners. The -axis shows the average tree depth for proteins with the given number of interaction partners ( -axis) in . Highly connected proteins tend to duplicate less than proteins with fewer interaction partners.</p>", "links"=>[], "tags"=>["computational biology/systems biology", "computer science", "molecular biology/bioinformatics"], "article_id"=>454868, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.g010"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_The_evolution_of_protein_duplication_/454868", "title"=>"The evolution of protein duplication.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:16:49"}
• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/784587/Figure_11.tif"], "description"=>"<p>The PA model appears most applicable to reversing the network.</p>", "links"=>[], "tags"=>["node", "times", "users"], "article_id"=>454963, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.g011"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Predicting_node_arrival_times_for_users_in_the_Last_fm_social_network_/454963", "title"=>"Predicting node arrival times for users in the Last.fm social network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:17:17"}
• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/784659/Figure_12.tif"], "description"=>"<p>Computing the similarity of node/anchor pairs in the true versus the reconstructed histories.</p>", "links"=>[], "tags"=>["pairs", "reconstructed"], "article_id"=>455034, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.g012"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Computing_the_similarity_of_node_anchor_pairs_in_the_true_versus_the_reconstructed_histories_/455034", "title"=>"Computing the similarity of node/anchor pairs in the true versus the reconstructed histories.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:17:44"}
• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/784700/Table_2.xls"], "description"=>"<p>Column headings show the model and parameters used to grow the random graph forward. Row labels show the model used in the reversal (assuming optimal parameters). For the node degree reconstruction ( row), we removed nodes in increasing order of their degree in the extant network (nodes with the same degree were ordered randomly). For the centrality reconstruction ( row), we removed nodes in decreasing order of their closeness centrality in the extant network. Each cell contains Anchor/Footrule scores (PA, node degree, and centrality do not generate Anchor scores). Performance was averaged over 1000 runs. Bolded cells indicate best performance. For example, for DMC random graphs with , reversing with FF produces a 33.1% Footrule score compared to a 45.5% score when the graph is reversed with DMC itself. The non-model-based heuristics produce good age-estimates when applied to models where degree is known to be correlated with age (FF and PA) as is expected; however, the downside to these approaches is that they do not produce a likelihood estimate for ancestral graphs, nor do they predict node anchors. For identifying anchors and for DMC age estimates, reversing with the model used to grow the graph forward resulted in the best performance.</p>", "links"=>[], "tags"=>["models"], "article_id"=>455078, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_Validating_network_growth_models_via_the_confusion_matrix_/455078", "title"=>"Validating network growth models via the confusion matrix.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 19:17:59"}
• {"files"=>["https://s3-eu-west-1.amazonaws.com/pstorage-plos-3567654/784733/Table_1.xls"], "description"=>"<p>Column headings show the value used in the modified DMC model and the three reconstruction quality measures. Kendall and Footrule are only computed among extant nodes. The other DMC parameters are fixed to and . As increases, more nodes are lost in the forward growth procedure. This degrades the accuracy of reconstruction because the algorithm is forced to explain interaction partners from anchors that do not exist in . This results in incorrect merges and incorrect ancestral edges. Nonetheless, the algorithm can tolerate deletion at low values of . In particular, when (i.e. in each step, with probability 0.3, a random node is deleted), the Kendall and Anchor quality measures decreases by only half of their original values.</p>", "links"=>[], "tags"=>["node", "deletion", "reconstruction"], "article_id"=>455113, "categories"=>["Information And Computing Sciences", "Medicine", "Molecular Biology"], "users"=>["Saket Navlakha", "Carl Kingsford"], "doi"=>["http://dx.doi.org/10.1371/journal.pcbi.1001119.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"http://figshare.com/articles/_The_effect_of_node_deletion_on_reconstruction_quality_/455113", "title"=>"The effect of node deletion on reconstruction quality.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 19:18:07"}

PMC Usage Stats | Further Information

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• {"unique-ip"=>"3", "full-text"=>"2", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
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• {"unique-ip"=>"4", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
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• {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
• {"unique-ip"=>"8", "full-text"=>"8", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
• {"unique-ip"=>"5", "full-text"=>"7", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
• {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
• {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
• {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
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Relative Metric

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F1000Prime