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A Protein Domain Co-Occurrence Network Approach for Predicting Protein Function and Inferring Species Phylogeny

Publication Date: March 24, 2011
Journal: PLOS ONE
Authors: Zheng Wang, Xue Cheng Zhang, Mi Ha Le, Dong Xu, et al
Volume: 6
Issue: 3
Pages: e17906
DOI: https://dx.plos.org/10.1371/journal.pone.0017906
Publisher URL: http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0017906
PubMed: http://www.ncbi.nlm.nih.gov/pubmed/21455299
PubMed Central: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3063783
Europe PMC: http://europepmc.org/abstract/MED/21455299
Web of Science: 000288811500014
Scopus: 79953032790
Mendeley: http://www.mendeley.com/research/protein-domain-cooccurrence-network-approach-predicting-protein-function-inferring-species-phylogeny

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{"title"=>"A protein domain Co-occurrence network approach for predicting protein function and inferring species phylogeny", "type"=>"journal", "authors"=>[{"first_name"=>"Zheng", "last_name"=>"Wang", "scopus_author_id"=>"56118014400"}, {"first_name"=>"Xue Cheng", "last_name"=>"Zhang", "scopus_author_id"=>"56645021400"}, {"first_name"=>"Mi Ha", "last_name"=>"Le", "scopus_author_id"=>"47461381900"}, {"first_name"=>"Dong", "last_name"=>"Xu", "scopus_author_id"=>"14323975400"}, {"first_name"=>"Gary", "last_name"=>"Stacey", "scopus_author_id"=>"7102445573"}, {"first_name"=>"Jianlin", "last_name"=>"Cheng", "scopus_author_id"=>"9333555700"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "issn"=>"19326203", "pui"=>"361500605", "sgr"=>"79953032790", "pmid"=>"21455299", "scopus"=>"2-s2.0-79953032790", "doi"=>"10.1371/journal.pone.0017906"}, "id"=>"7c6d14fc-585e-3f63-82fd-8bf826e9a910", "abstract"=>"Protein Domain Co-occurrence Network (DCN) is a biological network that has not been fully-studied. We analyzed the properties of the DCNs of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes. These DCNs have the hallmark features of scale-free networks. We investigated the possibility of using DCNs to predict protein and domain functions. Based on our experiment conducted on 66 randomly selected proteins, the best of top 3 predictions made by our DCN-based aggregated neighbor-counting method achieved a semantic similarity score of 0.81 to the actual Gene Ontology terms of the proteins. Moreover, the top 3 predictions using neighbor-counting, χ(2), and a SVM-based method achieved an accuracy of 66%, 59%, and 61%, respectively, when used to predict specific Gene Ontology terms of human target domains. These predictions on average had a semantic similarity score of 0.82, 0.80, and 0.79 to the actual Gene Ontology terms, respectively. We also used DCNs to predict whether a domain is an enzyme domain, and our SVM-based and neighbor-inference method correctly classified 79% and 77% of the target domains, respectively. When using DCNs to classify a target domain into one of the six enzyme classes, we found that, as long as there is one EC number available in the neighboring domains, our SVM-based and neighboring-counting method correctly classified 92.4% and 91.9% of the target domains, respectively. Furthermore, we benchmarked the performance of using DCNs to infer species phylogenies on six different combinations of 398 single-chromosome prokaryotic genomes. The phylogenetic tree of 54 prokaryotic taxa generated by our DCNs-alignment-based method achieved a 93.45% similarity score compared to the Bergey's taxonomy. In summary, our studies show that genome-wide DCNs contain rich information that can be effectively used to decipher protein function and reveal the evolutionary relationship among species.", "link"=>"http://www.mendeley.com/research/protein-domain-cooccurrence-network-approach-predicting-protein-function-inferring-species-phylogeny", "reader_count"=>46, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>14, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>2, "Other"=>2, "Student > Master"=>1, "Student > Bachelor"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>14, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>15, "Student > Postgraduate"=>2, "Other"=>2, "Student > Master"=>1, "Student > Bachelor"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Engineering"=>1, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>6, "Agricultural and Biological Sciences"=>29, "Medicine and Dentistry"=>1, "Social Sciences"=>1, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>29}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>1, "China"=>1, "Brazil"=>1, "Denmark"=>1, "United Kingdom"=>2, "Australia"=>1, "Spain"=>1}, "group_count"=>2}

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{"files"=>["https://ndownloader.figshare.com/files/394841", "https://ndownloader.figshare.com/files/394868", "https://ndownloader.figshare.com/files/394889"], "description"=>"<div>Protein Domain Co-occurrence Network (DCN) is a biological network that has not been fully-studied. We analyzed the properties of the DCNs of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes. These DCNs have the hallmark features of scale-free networks. We investigated the possibility of using DCNs to predict protein and domain functions. Based on our experiment conducted on 66 randomly selected proteins, the best of top 3 predictions made by our DCN-based aggregated neighbor-counting method achieved a semantic similarity score of 0.81 to the actual Gene Ontology terms of the proteins. Moreover, the top 3 predictions using neighbor-counting, χ2, and a SVM-based method achieved an accuracy of 66%, 59%, and 61%, respectively, when used to predict specific Gene Ontology terms of human target domains. These predictions on average had a semantic similarity score of 0.82, 0.80, and 0.79 to the actual Gene Ontology terms, respectively. We also used DCNs to predict whether a domain is an enzyme domain, and our SVM-based and neighbor-inference method correctly classified 79% and 77% of the target domains, respectively. When using DCNs to classify a target domain into one of the six enzyme classes, we found that, as long as there is one EC number available in the neighboring domains, our SVM-based and neighboring-counting method correctly classified 92.4% and 91.9% of the target domains, respectively. Furthermore, we benchmarked the performance of using DCNs to infer species phylogenies on six different combinations of 398 single-chromosome prokaryotic genomes. The phylogenetic tree of 54 prokaryotic taxa generated by our DCNs-alignment-based method achieved a 93.45% similarity score compared to the Bergey's taxonomy. In summary, our studies show that genome-wide DCNs contain rich information that can be effectively used to decipher protein function and reveal the evolutionary relationship among species. </div>", "links"=>[], "tags"=>["co-occurrence", "predicting", "inferring", "phylogeny"], "article_id"=>137994, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.s001", "https://dx.doi.org/10.1371/journal.pone.0017906.s002", "https://dx.doi.org/10.1371/journal.pone.0017906.s003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_Protein_Domain_Co_Occurrence_Network_Approach_for_Predicting_Protein_Function_and_Inferring_Species_Phylogeny/137994", "title"=>"A Protein Domain Co-Occurrence Network Approach for Predicting Protein Function and Inferring Species Phylogeny", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-03-24 02:13:14"}
{"files"=>["https://ndownloader.figshare.com/files/788307"], "description"=>"Left: Protein ubiquinone oxidoreductase, which contains four Pfam domains. Right: Protein xanthine dehydrogenase, which has six Pfam domains. The two proteins share the same domain Fer2. An edge is drawn between domains co-occurring in the same protein. A fully connected sub-graph (clique) in the DCN corresponds to a protein.", "links"=>[], "tags"=>["dcn", "consisting", "arabidopsis"], "article_id"=>458674, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_small_DCN_consisting_of_two_Arabidopsis_proteins_/458674", "title"=>"A small DCN consisting of two Arabidopsis proteins.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-24 02:24:34"}
{"files"=>["https://ndownloader.figshare.com/files/788419"], "description"=>"(A) The degree distribution plots (scale-free); (B) the distributions of lengths of the shortest paths (small-world); (C) the log-log plots of clustering coefficients against degrees (hierarchical modularity).", "links"=>[], "tags"=>["properties", "dcns"], "article_id"=>458788, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_properties_of_DCNs_of_four_representative_species_/458788", "title"=>"Statistical properties of DCNs of four representative species.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-24 02:26:28"}
{"files"=>["https://ndownloader.figshare.com/files/788503"], "description"=>"The more neighors with known functions, the higher accuracies neighor-counting method can achieve when pedicting the functions of the central node. Most of the target domains have less than five neigbors with known functions.", "links"=>[], "tags"=>["neighbors", "functions", "neighbor-counting"], "article_id"=>458867, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_number_of_neighbors_with_known_functions_versus_the_prediction_accuracy_of_the_neighbor_counting_method_/458867", "title"=>"The number of neighbors with known functions versus the prediction accuracy of the neighbor-counting method.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-24 02:27:47"}
{"files"=>["https://ndownloader.figshare.com/files/788578"], "description"=>"The query protein for this example contains 1,225 amino acids and has an ID “DDB_G0274191” in DictyBase (<a href=\"http://dictybase.org/gene/DDB_G0274191\" target=\"_blank\">http://dictybase.org/gene/DDB_G0274191</a>). Three unique Pfam domains (red vertices) were detected by HHsearch, each with an e-value< = 0.01. It had a PSI-BLAST hit to a protein of Vitis vinifera (grape) with an e-value e-118. Therefore, the DCN of Vitis vinifera was used to make predictions. The vertices in yellow are the radius one neighboring vertices of the three Pfam domains. The GO terms of these yellow vertices were put together and ranked based on their occurrence frequencies. This query protein contains 14 real GO terms. Everyone of the top 3 predictions (GO:0005524, GO:0006355, and GO:0016020) ranked by DCN-based aggregated neighbor-counting method matches one of the real GO terms. Besides that, the 5th (GO:0007165), 7th (GO:0000155), 13th (GO:0000160), and 14th (GO:0000156) ranked GO terms have an exact match to the real GO terms.", "links"=>[], "tags"=>["dcn-based", "aggregated", "neighbor-counting"], "article_id"=>458940, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_example_showing_the_DCN_based_aggregated_neighbor_counting_method_for_protein_function_prediction_/458940", "title"=>"An example showing the DCN-based aggregated neighbor-counting method for protein function prediction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-24 02:29:00"}
{"files"=>["https://ndownloader.figshare.com/files/788731"], "description"=>"An example illustrating the graph alignment algorithm we utilized.", "links"=>[], "tags"=>["illustrating", "graph", "alignment", "algorithm"], "article_id"=>459096, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_example_illustrating_the_graph_alignment_algorithm_we_utilized_/459096", "title"=>"An example illustrating the graph alignment algorithm we utilized.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-24 02:31:36"}
{"files"=>["https://ndownloader.figshare.com/files/788797"], "description"=>"For each organism, the Bergey's code, NCBI ID, and the scientific name are shown. The percentage of the agreed quartets between this tree and the Bergey's taxonomy is 93.45%.", "links"=>[], "tags"=>["phylogenetic", "generated", "54", "single-chromosome", "prokaryotic", "taxa", "dcns-alignment-based", "inference"], "article_id"=>459155, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_phylogenetic_tree_generated_on_54_single_chromosome_prokaryotic_taxa_by_our_DCNs_alignment_based_inference_method_/459155", "title"=>"The phylogenetic tree generated on 54 single-chromosome prokaryotic taxa by our DCNs-alignment-based inference method.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-24 02:32:35"}
{"files"=>["https://ndownloader.figshare.com/files/788930"], "description"=>"In total, the DCN of Aradbisopsis contains 141 disconnected sub-graphs (each one has no edges connecting to any other sub-graphs); and most of the sub-graphs have less than 10 nodes. (A) is the largest DCN sub-graph, or the main graph, of Arabidopsis DCNs that has 626 nodes and 1,304 edges. The graphs shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone-0017906-g001\" target=\"_blank\">Figure 1</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone-0017906-g008\" target=\"_blank\">8</a> are two acutal examples of the small sub-graphs in Arabidopsis DCN. (B) is an enlarged partial view of Arabidopsis main DCN, in which domain Pkinase is a hub.", "links"=>[], "tags"=>["co-occurrence", "arabidopsis"], "article_id"=>459301, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Domain_co_occurrence_network_of_Arabidopsis_thaliana_/459301", "title"=>"Domain co-occurrence network of Arabidopsis thaliana.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-24 02:35:01"}
{"files"=>["https://ndownloader.figshare.com/files/789054"], "description"=>"Each red eclipse encircles the domains co-occurred in one protein.", "links"=>[], "tags"=>["sub-graphs"], "article_id"=>459426, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.g008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_relationship_between_sub_graphs_and_protein_function_/459426", "title"=>"The relationship between sub-graphs and protein function.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-24 02:37:06"}
{"files"=>["https://ndownloader.figshare.com/files/789103"], "description"=>"The DCN is supposed to have only four domains: a, b, c, and d, and the functions of domain a are treated unknown. It only shows the binary classification process for GO term 2. This same process should be applied on every GO term (GO term 1–7) that occurred in the training domains (domain b, c, and d). The top ranked GO term(s) is (are) treated as the final predicted functions.", "links"=>[], "tags"=>["svm"], "article_id"=>459475, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.g009"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicting_the_GO_terms_of_domain_a_using_SVM_classification_/459475", "title"=>"Predicting the GO terms of domain a using SVM classification.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-24 02:37:55"}
{"files"=>["https://ndownloader.figshare.com/files/789175"], "description"=>"Composition details of the 398 single-chromosome prokaryotic genomes (strains).", "links"=>[], "tags"=>["details", "398", "single-chromosome", "prokaryotic", "genomes"], "article_id"=>459542, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.g010"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Composition_details_of_the_398_single_chromosome_prokaryotic_genomes_strains_/459542", "title"=>"Composition details of the 398 single-chromosome prokaryotic genomes (strains).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-24 02:39:02"}
{"files"=>["https://ndownloader.figshare.com/files/789266"], "description"=>"For SVM, it reports the accuracy of a leave-one-domain-out cross-validation. Experiments were performed on Arabidopsis DCNs. “Known EC” indicates the number of known EC numbers occurrences in the radius one neighboring domains. “Target Number” indicates the number of target domains. The features used in SVM-based method are the GO and EC number occurrence-frequencies, with the linear kernel function.", "links"=>[], "tags"=>["neighbor-counting", "svm-base", "predicting", "ec"], "article_id"=>459633, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t010"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_accuracy_of_the_neighbor_counting_and_SVM_base_method_when_predicting_EC_families_/459633", "title"=>"Prediction accuracy of the neighbor-counting and SVM-base method when predicting EC families.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 02:40:33"}
{"files"=>["https://ndownloader.figshare.com/files/789311"], "description"=>"The ways of calculating precision and recall can be found at the captions of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone-0017906-t004\" target=\"_blank\">Table 4</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone-0017906-t003\" target=\"_blank\">Table 3</a>, respectively. Explanations of the best semantic similarity score can be found at the caption of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone-0017906-t002\" target=\"_blank\">Table 2</a>. From the 100 proteins randomly selected from GO database, 66 proteins have predictions available by DCN-based aggregated neighbor-counting method.", "links"=>[], "tags"=>["dcn-based", "aggregated", "neighbor-counting", "66", "randomly"], "article_id"=>459673, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t011"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_results_of_DCN_based_aggregated_neighbor_counting_method_on_66_randomly_selected_proteins_/459673", "title"=>"The results of DCN-based aggregated neighbor-counting method on 66 randomly selected proteins.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 02:41:13"}
{"files"=>["https://ndownloader.figshare.com/files/789348"], "description"=>"The composition of dataset 6 containing 54 single-chromosome prokaryotic organisms.", "links"=>[], "tags"=>["dataset", "containing", "54", "single-chromosome", "prokaryotic"], "article_id"=>459714, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t014"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_composition_of_dataset_6_containing_54_single_chromosome_prokaryotic_organisms_/459714", "title"=>"The composition of dataset 6 containing 54 single-chromosome prokaryotic organisms.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 02:41:54"}
{"files"=>["https://ndownloader.figshare.com/files/789378"], "description"=>"Definition of best prediction can be found at the caption of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone-0017906-t002\" target=\"_blank\">Table 2</a>. “N/A” indicates this specific domain does not exist in the DCN of a species. “Average” indicates the average value of degree values and similarity scores. “Arab.” indicates “Arabidopsis”, and “Hum.” indicates “Human”. Some of these promiscuous domains were selected from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone.0017906-Basu1\" target=\"_blank\">[42]</a>.", "links"=>[], "tags"=>["neighbor-counting", "promiscuous"], "article_id"=>459748, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performances_of_neighbor_counting_method_on_promiscuous_domains_/459748", "title"=>"Performances of neighbor-counting method on promiscuous domains.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 02:42:28"}
{"files"=>["https://ndownloader.figshare.com/files/789411"], "description"=>"Precision was calculated as the number of correctly predicted GO terms divided by the number of predictions a method made (in this case, three). Average precision was the sum of precision value for each domain divided by the total number of test domains. The precisions of “Top 1” can be found at <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone-0017906-t001\" target=\"_blank\">Table 1</a>.", "links"=>[], "tags"=>["precision", "svm"], "article_id"=>459781, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_average_precision_value_of_using_neighbor_counting_967_2_and_SVM_for_top_3_predictions_/459781", "title"=>"The average precision value of using neighbor counting, χ2, and SVM for top 3 predictions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 02:43:01"}
{"files"=>["https://ndownloader.figshare.com/files/789446"], "description"=>"Definition of the best prediction can be found at the caption of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone-0017906-t002\" target=\"_blank\">Table 2</a>. The degree value of each domain can be found at <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone-0017906-t005\" target=\"_blank\">Table 5</a>.", "links"=>[], "tags"=>["svm-based", "promiscuous"], "article_id"=>459818, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performances_of_SVM_based_method_on_promiscuous_domains_/459818", "title"=>"Performances of SVM-based method on promiscuous domains.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 02:43:38"}
{"files"=>["https://ndownloader.figshare.com/files/789477"], "description"=>"Definition of the best prediction can be found at the caption of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone-0017906-t002\" target=\"_blank\">Table 2</a>. The degree value of each promiscuous domain can be found at <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone-0017906-t005\" target=\"_blank\">Table 5</a>.", "links"=>[], "tags"=>["promiscuous"], "article_id"=>459849, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performances_of_967_2_method_on_promiscuous_domains_/459849", "title"=>"Performances of χ2 method on promiscuous domains.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 02:44:09"}
{"files"=>["https://ndownloader.figshare.com/files/789510"], "description"=>"For SVM, it's the average accuracy of a leave-one-domain-out cross-validation. “Top 1” (“Top 3”) indicates when “the top one ranked GO term” (“one of the top three ranked GO terms”) matches one of the actual GO terms of the target domain, it is considered a correct prediction. The values shown under “Top 1” are also the precision values for top 1 prediction. The precision values for “Top 3” predictions can be found at <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone-0017906-t004\" target=\"_blank\">Table 4</a>.", "links"=>[], "tags"=>["svm", "predicting"], "article_id"=>459880, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_prediction_accuracy_of_using_neighbor_counting_967_2_and_SVM_when_predicting_GO_terms_/459880", "title"=>"The prediction accuracy of using neighbor counting, χ2, and SVM when predicting GO terms.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 02:44:40"}
{"files"=>["https://ndownloader.figshare.com/files/789545"], "description"=>"The accuracy of OG, CVT, SDD, and CD are directly retrieved from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone.0017906-Lin1\" target=\"_blank\">[50]</a>. Accuracies are reflected by the percentage of the agreed quartets.1OG = Overlapping Gene Distance <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone.0017906-Luo1\" target=\"_blank\">[46]</a>,2CVT = Composition Vector Tree <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone.0017906-Gao1\" target=\"_blank\">[47]</a>, and k is the length of string,3CCV = Complete Composition Vector <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone.0017906-Wu2\" target=\"_blank\">[48]</a>,4PDUG = Structural Protein Domain Universe Graph <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone.0017906-Deeds1\" target=\"_blank\">[49]</a>,5CD = Composite Distance <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone.0017906-Lin1\" target=\"_blank\">[50]</a>.", "links"=>[], "tags"=>["comparisons", "dcns-based", "methods", "phylogeny"], "article_id"=>459909, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t013"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accuracy_comparisons_between_our_DCNs_based_method_and_other_methods_for_phylogeny_inference_/459909", "title"=>"Accuracy comparisons between our DCNs-based method and other methods for phylogeny inference.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 02:45:09"}
{"files"=>["https://ndownloader.figshare.com/files/789575"], "description"=>"Recall was calculated as the correctly predicted GO terms divided by the total number of real GO terms. Average recall was the sum of recall value for each domain divided by the total number of test domains.", "links"=>[], "tags"=>["svm", "predicting"], "article_id"=>459947, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_average_recall_value_of_using_neighbor_counting_967_2_and_SVM_when_predicting_GO_terms_/459947", "title"=>"The average recall value of using neighbor counting, χ2, and SVM when predicting GO terms.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 02:45:47"}
{"files"=>["https://ndownloader.figshare.com/files/789604"], "description"=>"“Top 1” (“Top 3”) indicates that for each target domain, we calculate the pair-wise similarity scores between the top one (top three) ranked GO term(s) and the actual GO terms, and the highest score is considered as the similarity score of the best prediction. Semantic scores are calculated by the tool G-SESAME <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017906#pone.0017906-Du1\" target=\"_blank\">[39]</a>.", "links"=>[], "tags"=>["semantic", "scores"], "article_id"=>459981, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_average_semantic_similarity_scores_of_the_best_predictions_among_8220_Top_1_8221_or_8220_Top_3_8221_GO_term_s_/459981", "title"=>"The average semantic similarity scores of the best predictions, among “Top 1” or “Top 3” GO term(s).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 02:46:21"}
{"files"=>["https://ndownloader.figshare.com/files/789643"], "description"=>"The results of DCN-based aggregated neighbor-counting method on 9 single-domain proteins.", "links"=>[], "tags"=>["dcn-based", "aggregated", "neighbor-counting", "single-domain"], "article_id"=>460009, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t012"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_results_of_DCN_based_aggregated_neighbor_counting_method_on_9_single_domain_proteins_/460009", "title"=>"The results of DCN-based aggregated neighbor-counting method on 9 single-domain proteins.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 00:00:09"}
{"files"=>["https://ndownloader.figshare.com/files/789679"], "description"=>"The “Ratio” indicates the percentage of correctly predicted domains.", "links"=>[], "tags"=>["accuracies", "enzyme"], "article_id"=>460048, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t009"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_accuracies_of_using_8220_neighbor_inference_8221_method_for_enzyme_8220_yes_or_no_8221_predictions_/460048", "title"=>"The accuracies of using “neighbor-inference” method for enzyme “yes or no” predictions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 00:00:48"}
{"files"=>["https://ndownloader.figshare.com/files/789713"], "description"=>"“Ratio” standards for the percentage of correctly predicted domains in the cross-validation. The feature used are only GO term frequencies gained from radius one neighboring domains.", "links"=>[], "tags"=>["leave-one-out", "cross-validation", "svm-based", "enzyme"], "article_id"=>460083, "categories"=>["Biological Sciences", "Genetics", "Evolutionary Biology"], "users"=>["Zheng Wang", "Xue-Cheng Zhang", "Mi Ha Le", "Dong Xu", "Gary Stacey", "Jianlin Cheng"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017906.t008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_leave_one_out_cross_validation_results_of_the_SVM_based_enzyme_8220_yes_or_no_8221_predictions_/460083", "title"=>"The leave-one-out cross-validation results of the SVM-based enzyme “yes or no” predictions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-24 00:01:23"}

PMC Usage Stats 103317 Jan 15:26 UTC

PMC Usage Stats | Further Information

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Relative Metric 35659204 Apr 12:19 UTC

Relative Metric

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F1000Prime13 Jan 12:22 UTC

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