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Mendeley | Further Information

{"title"=>"TargetMine, an integrated data warehouse for candidate gene prioritisation and target discovery", "type"=>"journal", "authors"=>[{"first_name"=>"Yi An", "last_name"=>"Chen", "scopus_author_id"=>"55895369000"}, {"first_name"=>"Lokesh P.", "last_name"=>"Tripathi", "scopus_author_id"=>"14525859300"}, {"first_name"=>"Kenji", "last_name"=>"Mizuguchi", "scopus_author_id"=>"7101693768"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"361404882", "sgr"=>"79952392088", "issn"=>"19326203", "pmid"=>"21408081", "scopus"=>"2-s2.0-79952392088", "doi"=>"10.1371/journal.pone.0017844", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"767b86c1-ec40-3ac3-84de-0d4e4a8a58cc", "abstract"=>"Prioritising candidate genes for further experimental characterisation is a non-trivial challenge in drug discovery and biomedical research in general. An integrated approach that combines results from multiple data types is best suited for optimal target selection. We developed TargetMine, a data warehouse for efficient target prioritisation. TargetMine utilises the InterMine framework, with new data models such as protein-DNA interactions integrated in a novel way. It enables complicated searches that are difficult to perform with existing tools and it also offers integration of custom annotations and in-house experimental data. We proposed an objective protocol for target prioritisation using TargetMine and set up a benchmarking procedure to evaluate its performance. The results show that the protocol can identify known disease-associated genes with high precision and coverage. A demonstration version of TargetMine is available at http://targetmine.nibio.go.jp/.", "link"=>"http://www.mendeley.com/research/targetmine-integrated-data-warehouse-candidate-gene-prioritisation-target-discovery", "reader_count"=>74, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Librarian"=>2, "Researcher"=>16, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>3, "Student > Master"=>7, "Other"=>10, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Librarian"=>2, "Researcher"=>16, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>3, "Student > Master"=>7, "Other"=>10, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>8, "Agricultural and Biological Sciences"=>36, "Medicine and Dentistry"=>10, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>1, "Computer Science"=>13, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>10}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>36}, "Computer Science"=>{"Computer Science"=>13}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}, "Unspecified"=>{"Unspecified"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Canada"=>2, "Korea (South)"=>1, "Brazil"=>1, "United Kingdom"=>2, "France"=>1, "Tunisia"=>1}, "group_count"=>0}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/792669"], "description"=>"<p>(The full disease names and their abbreviations are listed in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017844#pone-0017844-t002\" target=\"_blank\">Table 2</a>.) Each line\n represents the F-score for a particular disease data set as a function\n of the threshold (the top <i>N</i> significant associations\n considered). The error bars show the standard deviation across ten\n benchmarking evaluations for each disease.</p>", "links"=>[], "tags"=>["19", "sets", "disease-associated"], "article_id"=>463043, "categories"=>["Biotechnology", "Biochemistry", "Genetics", "Biological Sciences"], "users"=>["Yi-An Chen", "Lokesh P. Tripathi", "Kenji Mizuguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017844.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Benchmarking_results_for_19_sets_of_known_disease_associated____genes_/463043", "title"=>"Benchmarking results for 19 sets of known disease-associated\n genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-08 00:50:43"}
• {"files"=>["https://ndownloader.figshare.com/files/792621"], "description"=>"<p>TP- True positive, FP- False positive (see text for details).</p>", "links"=>[], "tags"=>["benchmarking", "gene", "prioritisation", "19", "sets", "disease-associated", "genes", "with"], "article_id"=>462978, "categories"=>["Biotechnology", "Biochemistry", "Genetics", "Biological Sciences"], "users"=>["Yi-An Chen", "Lokesh P. Tripathi", "Kenji Mizuguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017844.g003", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Outline_of_the_procedure_for_benchmarking_candidate_gene____prioritisation_on_19_sets_of_known_disease_associated_genes_with____TargetMine_/462978", "title"=>"Outline of the procedure for benchmarking candidate gene\n prioritisation on 19 sets of known disease-associated genes with\n TargetMine.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-08 00:49:38"}
• {"files"=>["https://ndownloader.figshare.com/files/792408"], "description"=>"<p>The data model is depicted as a class diagram in the Unified Modeling\n Language (<a href=\"http://www.uml.org\" target=\"_blank\">http://www.uml.org</a>). Some details of the model are\n ignored to reduce the complexity of the diagram.</p>", "links"=>[], "tags"=>["examples", "created", "sources", "in"], "article_id"=>462780, "categories"=>["Biotechnology", "Biochemistry", "Genetics", "Biological Sciences"], "users"=>["Yi-An Chen", "Lokesh P. Tripathi", "Kenji Mizuguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017844.g001", "stats"=>{"downloads"=>2, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schema_for_selected_examples_of_newly_created_data_sources_in____TargetMine_/462780", "title"=>"Schema for selected examples of newly created data sources in\n TargetMine.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-08 00:46:20"}
• {"files"=>["https://ndownloader.figshare.com/files/792513"], "description"=>"<p>A schematic representation of the suggested objective protocol for\n candidate gene prioritisation with TargetMine.</p>", "links"=>[], "tags"=>["schematic", "suggested", "for", "prioritisation"], "article_id"=>462882, "categories"=>["Biotechnology", "Biochemistry", "Genetics", "Biological Sciences"], "users"=>["Yi-An Chen", "Lokesh P. Tripathi", "Kenji Mizuguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017844.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_schematic_representation_of_the_suggested_objective_protocol_for____candidate_gene_prioritisation_with_TargetMine_/462882", "title"=>"A schematic representation of the suggested objective protocol for\n candidate gene prioritisation with TargetMine.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-08 00:48:02"}
• {"files"=>["https://ndownloader.figshare.com/files/792715"], "description"=>"<p>*H: human, R: rat, M: mouse, F: fruit fly\n (<i>Drosophila</i>), E: <i>E. coli</i>.</p>†<p>OMIM data are presently not distributed with the TargetMine\n demonstration version.</p>", "links"=>[], "tags"=>["sources"], "article_id"=>463095, "categories"=>["Biotechnology", "Biochemistry", "Genetics", "Biological Sciences"], "users"=>["Yi-An Chen", "Lokesh P. Tripathi", "Kenji Mizuguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017844.t001", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_List_of_data_sources_in_TargetMine_/463095", "title"=>"List of data sources in TargetMine.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-08 00:51:35"}
• {"files"=>["https://ndownloader.figshare.com/files/396720", "https://ndownloader.figshare.com/files/396733", "https://ndownloader.figshare.com/files/396765", "https://ndownloader.figshare.com/files/396786"], "description"=>"<div><p>Prioritising candidate genes for further experimental characterisation is a non-trivial challenge in drug discovery and biomedical research in general. An integrated approach that combines results from multiple data types is best suited for optimal target selection. We developed TargetMine, a data warehouse for efficient target prioritisation. TargetMine utilises the InterMine framework, with new data models such as protein-DNA interactions integrated in a novel way. It enables complicated searches that are difficult to perform with existing tools and it also offers integration of custom annotations and in-house experimental data. We proposed an objective protocol for target prioritisation using TargetMine and set up a benchmarking procedure to evaluate its performance. The results show that the protocol can identify known disease-associated genes with high precision and coverage. A demonstration version of TargetMine is available at <a href=\"http://targetmine.nibio.go.jp/\">http://targetmine.nibio.go.jp/</a>.</p> </div>", "links"=>[], "tags"=>["gene", "prioritisation"], "article_id"=>138373, "categories"=>["Biotechnology", "Biochemistry", "Genetics", "Biological Sciences"], "users"=>["Yi-An Chen", "Lokesh P. Tripathi", "Kenji Mizuguchi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017844.s001", "https://dx.doi.org/10.1371/journal.pone.0017844.s002", "https://dx.doi.org/10.1371/journal.pone.0017844.s003", "https://dx.doi.org/10.1371/journal.pone.0017844.s004"], "stats"=>{"downloads"=>27, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/TargetMine_an_Integrated_Data_Warehouse_for_Candidate_Gene___Prioritisation_and_Target_Discovery/138373", "title"=>"TargetMine, an Integrated Data Warehouse for Candidate Gene\n Prioritisation and Target Discovery", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-03-08 02:19:33"}
• {"files"=>["https://ndownloader.figshare.com/files/792761"], "description"=>"<p>Benchmarking results for 19 sets of known disease-associated genes\n using top seven significant associations as the threshold.</p>", "links"=>[], "tags"=>["19", "sets", "disease-associated", "genes", "associations"], "article_id"=>463126, "categories"=>["Biotechnology", "Biochemistry", "Genetics", "Biological Sciences"], "users"=>["Yi-An Chen", "Lokesh P. Tripathi", "Kenji Mizuguchi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0017844.t002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Benchmarking_results_for_19_sets_of_known_disease_associated_genes____using_top_seven_significant_associations_as_the_threshold_/463126", "title"=>"Benchmarking results for 19 sets of known disease-associated genes\n using top seven significant associations as the threshold.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-08 00:52:06"}

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Relative Metric

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F1000Prime