Biomedical Discovery Acceleration, with Applications to Craniofacial Development
Publication Date
March 27, 2009
Journal
PLOS Computational Biology
Authors
Sonia M. Leach, Hannah Tipney, Weiguo Feng, William A. Baumgartner Jr., et al
Volume
5
Issue
3
Pages
e1000215
DOI
http://doi.org/10.1371/journal.pcbi.1000215
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1000215
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/19325874
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2653649
Europe PMC
http://europepmc.org/abstract/MED/19325874
Web of Science
000266214000001
Scopus
63749097353
Mendeley
http://www.mendeley.com/research/biomedical-discovery-acceleration-applications-craniofacial-development
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Mendeley | Further Information

{"title"=>"Biomedical discovery acceleration, with applications to craniofacial development", "type"=>"journal", "authors"=>[{"first_name"=>"Sonia M.", "last_name"=>"Leach", "scopus_author_id"=>"7103073596"}, {"first_name"=>"Hannah", "last_name"=>"Tipney", "scopus_author_id"=>"8282737900"}, {"first_name"=>"Weiguo", "last_name"=>"Feng", "scopus_author_id"=>"55290364600"}, {"first_name"=>"William A.", "last_name"=>"Baumgartner", "scopus_author_id"=>"55851941982"}, {"first_name"=>"Priyanka", "last_name"=>"Kasliwal", "scopus_author_id"=>"26428742500"}, {"first_name"=>"Ronald P.", "last_name"=>"Schuyler", "scopus_author_id"=>"26428939000"}, {"first_name"=>"Trevor", "last_name"=>"Williams", "scopus_author_id"=>"55993652800"}, {"first_name"=>"Richard A.", "last_name"=>"Spritz", "scopus_author_id"=>"7006548083"}, {"first_name"=>"Lawrence", "last_name"=>"Hunter", "scopus_author_id"=>"12647593500"}], "year"=>2009, "source"=>"PLoS Computational Biology", "identifiers"=>{"pmid"=>"19325874", "sgr"=>"63749097353", "issn"=>"1553734X", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "doi"=>"10.1371/journal.pcbi.1000215", "scopus"=>"2-s2.0-63749097353", "pui"=>"354416153"}, "id"=>"f359b1cf-2059-3e33-b8ea-9c27b66605ec", "abstract"=>"The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.", "link"=>"http://www.mendeley.com/research/biomedical-discovery-acceleration-applications-craniofacial-development", "reader_count"=>85, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>6, "Librarian"=>2, "Researcher"=>21, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>28, "Student > Postgraduate"=>3, "Student > Master"=>8, "Other"=>5, "Student > Bachelor"=>2, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>6, "Librarian"=>2, "Researcher"=>21, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>28, "Student > Postgraduate"=>3, "Student > Master"=>8, "Other"=>5, "Student > Bachelor"=>2, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>4, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>1, "Agricultural and Biological Sciences"=>30, "Medicine and Dentistry"=>10, "Arts and Humanities"=>1, "Physics and Astronomy"=>2, "Chemistry"=>2, "Psychology"=>2, "Social Sciences"=>1, "Computer Science"=>27}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>4}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>10}, "Chemistry"=>{"Chemistry"=>2}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>30}, "Computer Science"=>{"Computer Science"=>27}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>4}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Argentina"=>1, "Netherlands"=>1, "Belgium"=>1, "United States"=>15, "Brazil"=>2, "United Kingdom"=>1, "Slovenia"=>2, "Spain"=>2}, "group_count"=>9}

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/904723"], "description"=>"<p>The subnetwork can be seen to be separated into three functional\n clusters; physical integrity and generation of muscle, muscle\n differentiation and transcription, and neuron signaling and receptor\n interactions. Nodes and edges are colored as previously described in\n <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000215#pcbi-1000215-g009\" target=\"_blank\">Figure 9</a>.\n Nodes subject to further biological investigations are highlighted\n by opaque blue circles.</p>", "links"=>[], "tags"=>["clusters", "nodes", "mandibular", "specific"], "article_id"=>575171, "categories"=>["Developmental Biology", "Cancer", "Genetics", "Infectious Diseases", "Medicine"], "users"=>["Sonia M. Leach", "Hannah Tipney", "Weiguo Feng", "William A. Baumgartner Jr.", "Priyanka Kasliwal", "Ronald P. Schuyler", "Trevor Williams", "Richard A. Spritz", "Lawrence Hunter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000215.g011", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Functional_clusters_of_nodes_within_the_mandibular_specific_____sub_network_/575171", "title"=>"Functional clusters of nodes within the mandibular specific\n sub-network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-27 01:26:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/903390"], "description"=>"<p>A system diagram describing the modules of the Hanalyzer. Reading methods\n (green) take external sources of knowledge (blue) and extract information\n from them, either by parsing structured data or biomedical language\n processing to extract information from unstructured data. Reading modules\n are responsible for tracking the provenance of all knowledge. Reasoning\n methods (yellow) enrich the knowledge that results from reading by, for\n example, noting two genes that are annotated to the same ontology term or\n database entry. All knowledge sources, read or reasoned, are assigned a\n reliability score, and all are combined using that score into a knowledge\n network (orange) that represents the integration of all sorts of\n relationship between a pair of genes and a combined reliability score. A\n data network (also orange) is created from experimental results to be\n analyzed. The reporting modules (pink) integrate the data and knowledge\n networks, producing visualizations that can be queried with the associated\n drill-down tool.</p>", "links"=>[], "tags"=>["computational biology/genomics", "computational biology/literature analysis", "computational biology/systems biology", "developmental biology/developmental molecular mechanisms", "developmental biology/molecular development", "developmental biology/morphogenesis and cell biology", "genetics and genomics/bioinformatics", "genetics and genomics/gene discovery", "genetics and genomics/gene function"], "article_id"=>573831, "categories"=>["Developmental Biology", "Cancer", "Genetics", "Infectious Diseases", "Medicine"], "users"=>["Sonia M. Leach", "Hannah Tipney", "Weiguo Feng", "William A. Baumgartner Jr.", "Priyanka Kasliwal", "Ronald P. Schuyler", "Trevor Williams", "Richard A. Spritz", "Lawrence Hunter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000215.g001", "stats"=>{"downloads"=>2, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hanalyzer_system_diagram_/573831", "title"=>"Hanalyzer system diagram.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-27 01:03:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/904277"], "description"=>"<p>Relative expression of each gene is shown across five time points and\n three tissues, with red indicating higher expression and blue lower.\n Genes are grouped by protein family and clustered within these\n functional groups. Genes whose expression was classed as\n ‘absent’ in >99% of the\n samples are indicated by a red * and are included here for\n completion.</p>", "links"=>[], "tags"=>["genes"], "article_id"=>574728, "categories"=>["Developmental Biology", "Cancer", "Genetics", "Infectious Diseases", "Medicine"], "users"=>["Sonia M. Leach", "Hannah Tipney", "Weiguo Feng", "William A. Baumgartner Jr.", "Priyanka Kasliwal", "Ronald P. Schuyler", "Trevor Williams", "Richard A. Spritz", "Lawrence Hunter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000215.g008", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heatmap_of_genes_in_the_Average_sub_network_/574728", "title"=>"Heatmap of genes in the Average sub-network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-27 01:18:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/904565"], "description"=>"<p>Relative expression of each gene is shown across five time points and\n three tissues, with red indicating higher expression and blue lower.\n Genes are grouped by function and clustered within these subgroups.\n Those genes highlighted as candidates are indicated by a\n black*. Those genes whose expression was classed as\n ‘absent’ in >99% of the\n samples are indicated by a red* and are included here for\n completion.</p>", "links"=>[], "tags"=>["genes"], "article_id"=>575017, "categories"=>["Developmental Biology", "Cancer", "Genetics", "Infectious Diseases", "Medicine"], "users"=>["Sonia M. Leach", "Hannah Tipney", "Weiguo Feng", "William A. Baumgartner Jr.", "Priyanka Kasliwal", "Ronald P. Schuyler", "Trevor Williams", "Richard A. Spritz", "Lawrence Hunter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000215.g010", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heatmap_of_all_genes_in_the_sub_network_/575017", "title"=>"Heatmap of all genes in the sub-network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-27 01:23:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/446814", "https://ndownloader.figshare.com/files/446872"], "description"=>"<div><p>The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.</p> </div>", "links"=>[], "tags"=>["biomedical", "applications", "craniofacial"], "article_id"=>148074, "categories"=>["Developmental Biology", "Cancer", "Genetics", "Medicine"], "users"=>["Sonia M. Leach", "Hannah Tipney", "Weiguo Feng", "William A. Baumgartner Jr.", "Priyanka Kasliwal", "Ronald P. Schuyler", "Trevor Williams", "Richard A. Spritz", "Lawrence Hunter"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000215.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000215.s002"], "stats"=>{"downloads"=>15, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Biomedical_Discovery_Acceleration_with_Applications_to_Craniofacial___Development/148074", "title"=>"Biomedical Discovery Acceleration, with Applications to Craniofacial\n Development", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-03-27 02:14:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/903845"], "description"=>"<p>A) Histogram of edge probabilities for the experimental data component\n <i>P<sub>EXP</sub></i> (y-axis is 106 scale). B) Histogram\n of edge probabilities for the network component P<i><sub>NET</sub></i> (y-axis is 107 scale). C) Scatterplot comparing the two\n probability distributions where each point represents an edge. D) Top 1000\n scoring edges by either Average or Logit combination functions.</p>", "links"=>[], "tags"=>["probabilities"], "article_id"=>574298, "categories"=>["Developmental Biology", "Cancer", "Genetics", "Infectious Diseases", "Medicine"], "users"=>["Sonia M. Leach", "Hannah Tipney", "Weiguo Feng", "William A. Baumgartner Jr.", "Priyanka Kasliwal", "Ronald P. Schuyler", "Trevor Williams", "Richard A. Spritz", "Lawrence Hunter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000215.g005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_network_probabilities_and_their_combinations_/574298", "title"=>"Comparison of network probabilities and their combinations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-27 01:11:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/903545"], "description"=>"<p>Screenshot illustrating the use of the <i>CommonAtrributes</i>\n plugin developed to aid exploration of the knowledge network within\n Cytoscape. Here the linkage between two genes, <i>Des</i> and\n <i>Actc1</i> (yellow filled circles), is being explored.\n By right-clicking on the edge between these two genes, a drop down menu\n appears including the CommonAttributes2 label which points to the five\n experts (GO:BP, GO:CC, PHENO, KEGG and GO:MF) which support linking\n <i>Des</i> and <i>Actc1</i>. By selecting one of\n these experts, the attributes common to both genes from that expert are\n revealed. In this instance, it can be seen that <i>Des</i> and\n <i>Actc1</i> share seven phenotypic traits when knocked\n out or perturbed in mouse models.</p>", "links"=>[], "tags"=>["cytoscape"], "article_id"=>573997, "categories"=>["Developmental Biology", "Cancer", "Genetics", "Infectious Diseases", "Medicine"], "users"=>["Sonia M. Leach", "Hannah Tipney", "Weiguo Feng", "William A. Baumgartner Jr.", "Priyanka Kasliwal", "Ronald P. Schuyler", "Trevor Williams", "Richard A. Spritz", "Lawrence Hunter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000215.g002", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Visualisation_of_knowledge_network_via_Cytoscape_____CommonAttributes_plugin_/573997", "title"=>"Visualisation of knowledge network via Cytoscape\n <i>CommonAttributes</i> plugin.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-27 01:06:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/903646"], "description"=>"<p>The choice of probability combination functions in semantic integration\n and in the combination of knowledge and data networks is critical to the\n utility of the system. This figure shows the global characteristics of a\n variety of possible combination functions. Probabilities from two\n sources P1 and P2 (horizontal plane) are combined. Color indicates the\n magnitude of the combination (vertical axis) from 0.0 (blue) to 1.0\n (red). Application of Fisher's F, Mudholkar-George's T\n and Liptak-Stouffer's Z has been modified their treatment in\n <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000215#pcbi.1000215-Hwang1\" target=\"_blank\">[69]</a> to emphasize agreement on high\n probabilities rather than low p-values. The s and v parameters of the\n logistic function (Logit) were estimated as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000215#pcbi.1000215-Hanisch1\" target=\"_blank\">[80]</a>. The\n probability of a network edge given by the knowledgebase is calculated\n as described above, using the Noisy-OR function with the CONS\n reliability\n <i>P<sub>net</sub> = 1−Π<sub>i</sub>\n (1−r<sub>i</sub>)</i>. The probability from the\n external expression data source for an edge between two proteins\n <i>x</i> and <i>y</i> is simply the absolute\n value of the Pearson correlation coefficient computed between the\n expression profile vectors\n <i>P<sub>exp</sub> = |\n correlation(x,y) |</i>. The edge probabilities from the two\n sources are then combined either using the average of the probabilities\n Average<i> = [P<sub>net</sub>+P<sub>exp</sub>]/2</i>\n or the average of logistic functions of the probabilities\n Logit<i> = [logistic(P<sub>net</sub>)+logistic(P<sub>exp</sub>)]/2</i>\n where\n <i>logistic(X) = 1/(1−e<sup>−s(X−v)</sup>)</i>.\n As in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000215#pcbi.1000215-Hanisch1\" target=\"_blank\">[80]</a>, the parameter <i>v</i> is\n set to the mean of the corresponding distribution and the parameter\n <i>s</i> is set to <i>6/v</i> to yield a\n moderate slope. The reporting component of the system then uses the\n values of the combined function to extract sub-networks of high\n probability, either by including all edges exceeding a given score or\n the set of top scoring edges.</p>", "links"=>[], "tags"=>["probability"], "article_id"=>574098, "categories"=>["Developmental Biology", "Cancer", "Genetics", "Infectious Diseases", "Medicine"], "users"=>["Sonia M. Leach", "Hannah Tipney", "Weiguo Feng", "William A. Baumgartner Jr.", "Priyanka Kasliwal", "Ronald P. Schuyler", "Trevor Williams", "Richard A. Spritz", "Lawrence Hunter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000215.g003", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_probability_combination_functions_/574098", "title"=>"Comparison of probability combination functions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-27 01:08:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/903750"], "description"=>"<p>This figure illustrates the creation of the link between\n <i>MyoD1</i> and <i>MyoG</i> in the combined\n network. Nine experts are illustrated, including two language processing\n experts (blue), six experts based on inference from shared ontology\n annotations or database entries (orange), and one based on shared\n components from enriched ontology annotations (green). Each expert has a\n computed reliability (yellow), computed as described in the text. The\n identifiers in the expert boxes indicate the provenance of the\n inferences, with ellipses indicating omissions for space. The\n correlation between the expression levels of these two genes in the\n experimental data, <i>P<sub>data</sub></i>, is shown in\n purple. The Noisy-OR computation of the reliability from all knowledge\n sources is shown as <i>P<sub>knowledge</sub></i> and the two\n functions that combine the knowledge and data networks are show as\n <i>P<sub>average</sub></i> and\n <i>P<sub>logit</sub></i>. In this case,\n <i>P<sub>average</sub></i> was over the threshold for\n inclusion (top 1000 edges) in the combined grant, but\n <i>P<sub>logit</sub></i> was not.</p>", "links"=>[], "tags"=>["computational biology/genomics", "computational biology/literature analysis", "computational biology/systems biology", "developmental biology/developmental molecular mechanisms", "developmental biology/molecular development", "developmental biology/morphogenesis and cell biology", "genetics and genomics/bioinformatics", "genetics and genomics/gene discovery", "genetics and genomics/gene function"], "article_id"=>574195, "categories"=>["Developmental Biology", "Cancer", "Genetics", "Infectious Diseases", "Medicine"], "users"=>["Sonia M. Leach", "Hannah Tipney", "Weiguo Feng", "William A. Baumgartner Jr.", "Priyanka Kasliwal", "Ronald P. Schuyler", "Trevor Williams", "Richard A. Spritz", "Lawrence Hunter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000215.g004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Creating_a_link_in_the_combined_network_/574195", "title"=>"Creating a link in the combined network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-27 01:09:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/905042"], "description"=>"<p><i>In situ</i> hybridization using anti-sense probes for\n <i>Apobec2</i> (A–C),\n <i>E430002G05Rik</i> (D–F), <i>Hoxa2</i>\n (G–I), and <i>Zim1</i> (J, K). (A, D, G, J) sagittal\n sections of an E11.5 head; (B, C, E, F, H, I) are transverse sections of\n an E12.5 head. Anterior is to the right on all panels. Dark staining\n represents hybridization signal from the probe, the pink color is from a\n histological counterstain. The arrows indicate areas of fainter\n staining. (B, E, H) are more rostral sections than (C, F and I). The\n tongue has a mild convexity at these stages of development, being raised\n on its rostral aspect (see panels J). Therefore, more rostral sections\n will tend to skim the midsection of the tongue at the surface. More\n caudal sections will tend to intersect with staining patterns at their\n anterior and posterior domains (compare panels B and C).\n <i>Apobec2</i> and <i>E430002G05Rik</i> did not\n generate significant tongue staining at E11.5. Control experiments using\n sense probes did not yield specific staining. 1, mandibular component of\n first branchial arch (future lower jaw as well as future anterior and\n middle of tongue); 2, second branchial arch (future posterior, lateral,\n part of tongue – major site of <i>Hoxa2</i>\n expression); d, mandible; h, heart; hb, hindbrain; n, nasal prominence;\n ns, nasal septum; t, tongue; x, maxillary process.</p>", "links"=>[], "tags"=>["computational biology/genomics", "computational biology/literature analysis", "computational biology/systems biology", "developmental biology/developmental molecular mechanisms", "developmental biology/molecular development", "developmental biology/morphogenesis and cell biology", "genetics and genomics/bioinformatics", "genetics and genomics/gene discovery", "genetics and genomics/gene function"], "article_id"=>575482, "categories"=>["Developmental Biology", "Cancer", "Genetics", "Infectious Diseases", "Medicine"], "users"=>["Sonia M. Leach", "Hannah Tipney", "Weiguo Feng", "William A. Baumgartner Jr.", "Priyanka Kasliwal", "Ronald P. Schuyler", "Trevor Williams", "Richard A. Spritz", "Lawrence Hunter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000215.g012", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_expression_in_the_developing_mouse_tongue_/575482", "title"=>"Gene expression in the developing mouse tongue.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-27 01:31:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/904143"], "description"=>"<p>Graph illustrating the sub-network generated by viewing only those\n edges asserted by the Average combinatorial metric. A total of 20\n nodes and 50 edges are present; blue edges indicate those asserted\n solely by the Average metric, while red edges indicate those\n asserted by both Average and Logit metrics. Nodes are labeled by\n gene symbol with different node colors representing different\n protein families (Myosin, yellow; Actin, green; Troponin, purple).\n Colorless nodes indicate no common protein family.</p>", "links"=>[], "tags"=>["comprising", "edges", "asserted", "average", "combinatorial"], "article_id"=>574589, "categories"=>["Developmental Biology", "Cancer", "Genetics", "Infectious Diseases", "Medicine"], "users"=>["Sonia M. Leach", "Hannah Tipney", "Weiguo Feng", "William A. Baumgartner Jr.", "Priyanka Kasliwal", "Ronald P. Schuyler", "Trevor Williams", "Richard A. Spritz", "Lawrence Hunter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000215.g007", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sub_network_comprising_of_edges_asserted_by_the_Average_____combinatorial_metric_/574589", "title"=>"Sub-network comprising of edges asserted by the Average\n combinatorial metric.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-27 01:16:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/903950"], "description"=>"<p>Graph illustrating the network generated by taking the highest scoring\n 1000 edges as asserted by the Average (blue edges) and/or Logit (green\n edges) combinatorial measures. Blue edges indicate those edges asserted\n by both Average and Logit metrics. A total of 945 unique nodes (genes)\n and 1743 edges are shown (visualized in Cytoscape). The circled\n medium-sized sub-cluster to the right of the graph forms the basis of\n the investigations presented here (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000215#pcbi-1000215-g007\" target=\"_blank\">Figure 7</a>).</p>", "links"=>[], "tags"=>["1000", "edges", "asserted", "expression"], "article_id"=>574398, "categories"=>["Developmental Biology", "Cancer", "Genetics", "Infectious Diseases", "Medicine"], "users"=>["Sonia M. Leach", "Hannah Tipney", "Weiguo Feng", "William A. Baumgartner Jr.", "Priyanka Kasliwal", "Ronald P. Schuyler", "Trevor Williams", "Richard A. Spritz", "Lawrence Hunter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000215.g006", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graph_of_top_1000_edges_asserted_by_the_combination_of_expression____data_and_background_biological_knowledge_/574398", "title"=>"Graph of top 1000 edges asserted by the combination of expression\n data and background biological knowledge.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-27 01:13:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/904432"], "description"=>"<p>Graph illustrating the sub-network generated by viewing edges\n asserted by the both Average (blue edges) and Logit (green edges)\n combinatorial metrics. Red edges indicate those edges asserted by\n both metrics. Nodes are colored as previously described in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000215#pcbi-1000215-g007\" target=\"_blank\">Figure 7</a>.</p>", "links"=>[], "tags"=>["comprising", "edges", "asserted", "and", "logit"], "article_id"=>574879, "categories"=>["Developmental Biology", "Cancer", "Genetics", "Infectious Diseases", "Medicine"], "users"=>["Sonia M. Leach", "Hannah Tipney", "Weiguo Feng", "William A. Baumgartner Jr.", "Priyanka Kasliwal", "Ronald P. Schuyler", "Trevor Williams", "Richard A. Spritz", "Lawrence Hunter"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000215.g009", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sub_network_comprising_of_edges_asserted_by_both_Average_and_____Logit_combined_metrics_/574879", "title"=>"Sub-network comprising of edges asserted by both Average and\n Logit combined metrics.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-03-27 01:21:19"}

PMC Usage Stats | Further Information

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Relative Metric

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