1,720,976 research outputs found
In we present the number of modules determined by DMSP on various values of connectivity density
No full text<p><b>Copyright information:</b></p><p>Taken from "Growing functional modules from a seed protein via integration of protein interaction and gene expression data"</p><p>http://www.biomedcentral.com/1471-2105/8/408</p><p>BMC Bioinformatics 2007;8():408-408.</p><p>Published online 23 Oct 2007</p><p>PMCID:PMC2233647.</p><p></p> In blue dotted line we depict the number of functional modules, by keeping looser criteria for the determination of the modules. When we apply stricter criteria (as in red dotted line) there is a slight decrease in the number of the modules but at the same time the connectivity density values are better. In figure we display the initial size of the kernel, as well as the final size of the module, for modules with various sizes, and densities above 0.5. As we can see, DMSP manages to expand the initial size of the kernel in various degrees in order to determine the most coherent module each time
In this diagram we present the functional enrichment of modules in biological process GO terms
No full text<p><b>Copyright information:</b></p><p>Taken from "Growing functional modules from a seed protein via integration of protein interaction and gene expression data"</p><p>http://www.biomedcentral.com/1471-2105/8/408</p><p>BMC Bioinformatics 2007;8():408-408.</p><p>Published online 23 Oct 2007</p><p>PMCID:PMC2233647.</p><p></p> It is evident that the majority (75%) of the modules extracted by DMSP has p-value bins larger than 9, whereas 80% of the modules resulting from the PPI method (W&H) and 83% of the modules determined by clustering the co-expression network (CENC) have p-value bins ranging between 0 to 6
Growing functional modules from a seed protein via integration of protein interaction and gene expression data-5
No full text<p><b>Copyright information:</b></p><p>Taken from "Growing functional modules from a seed protein via integration of protein interaction and gene expression data"</p><p>http://www.biomedcentral.com/1471-2105/8/408</p><p>BMC Bioinformatics 2007;8():408-408.</p><p>Published online 23 Oct 2007</p><p>PMCID:PMC2233647.</p><p></p>present interactions that have been experimentally determined. In this figure, we give some examples of modules with less than 20 members (A, B, C) and modules with more than 20 members (D, E). In each one of these modules protein complexes were identified. This module contains the Arp2p/Arp3p complex the Replication Factor C complex the 20S Proteasome that was discovered in its entirety, the SRB-Srb10p complexes and the ADA-SAGA-TFIID complexes
Growing functional modules from a seed protein via integration of protein interaction and gene expression data-0
No full text<p><b>Copyright information:</b></p><p>Taken from "Growing functional modules from a seed protein via integration of protein interaction and gene expression data"</p><p>http://www.biomedcentral.com/1471-2105/8/408</p><p>BMC Bioinformatics 2007;8():408-408.</p><p>Published online 23 Oct 2007</p><p>PMCID:PMC2233647.</p><p></p>present interactions that have been experimentally determined. In this figure, we give some examples of modules with less than 20 members (A, B, C) and modules with more than 20 members (D, E). In each one of these modules protein complexes were identified. This module contains the Arp2p/Arp3p complex the Replication Factor C complex the 20S Proteasome that was discovered in its entirety, the SRB-Srb10p complexes and the ADA-SAGA-TFIID complexes
Scatter plots of statistical metrics for the derived and artificial functional modules
No full text<p><b>Copyright information:</b></p><p>Taken from "Growing functional modules from a seed protein via integration of protein interaction and gene expression data"</p><p>http://www.biomedcentral.com/1471-2105/8/408</p><p>BMC Bioinformatics 2007;8():408-408.</p><p>Published online 23 Oct 2007</p><p>PMCID:PMC2233647.</p><p></p> Each data point represents statistical value for a certain functional module (x-axis) and its artificially created corresponding module (y-axis). The red dashed line corresponds to the line y = x. When a data point is below the line then the artificial module has a lower statistical value than the derived one, while the opposite stands for the case a data point is above the line. When the data point is on the line it means that the derived and its corresponding artificial module have the same value. The metric used in this plot is connectivity density, which is a measure of how densely connected is a specific module. Representation of R measuring the coverage in protein complexes of a detected functional module. It is evident from both diagrams that in all cases the derived from DMSP functional modules have better statistical values than the artificial ones
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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