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Crossmaps: visualization of overlapping relationships in collections of journal papers.

ABSTRACT: A crossmapping technique is introduced for visualizing multiple and overlapping relations among entity types in collections of journal articles. Groups of entities from two entity types are crossplotted to show correspondence of relations. For example, author collaboration groups are plotted on the x axis against groups of papers (research fronts) on the y axis. At the intersection of each pair of author group/research front pairs a circular symbol is plotted whose size is proportional to the number of times that authors in the group appear as authors in papers in the research front. Entity groups are found by agglomerative hierarchical clustering using conventional similarity measures. Crossmaps comprise a simple technique that is particularly suited to showing overlap in relations among entity groups. Particularly useful crossmaps are: research fronts against base reference clusters, research fronts against author collaboration groups, and research fronts against term co-occurrence clusters. When exploring the knowledge domain of a collection of journal papers, it is useful to have several crossmaps of different entity pairs, complemented by research front timelines and base reference cluster timelines.

SUBMITTER: Morris SA

PROVIDER: S-EPMC387310 | biostudies-other | 2004 Apr

REPOSITORIES: biostudies-other

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Project description:BackgroundSponsors that seek to commercialize new drugs apply to the Food and Drug Administration (FDA) which independently analyzes the raw data and reports the results on its website.ObjectivesThis study sought to determine if there are differences between the FDA assessments and journal reports on biologic agents developed for the treatment of rheumatoid arthritis.MethodsAvailable data on FDA-approved drugs were extracted from the website, and a systematic literature search was conducted to identify matching studies in peer-reviewed medical journals. Outcome measures were the American College of Rheumatology response criteria ACR20 (efficacy) and withdrawal due to adverse events (safety). As effect size odds ratios were estimated for each active trial arm vs. control arm (i.e. for both sources: FDA and journal report), followed by calculation of the ratios of the FDA and journal report odds ratios. A ratio of odds ratios not equal to 1 was categorized as a discrepancy.ResultsFDA reports were available for 8 of 9 FDA-approved biologic agents for rheumatoid arthritis; all identified trials (34) except one were published in peer-reviewed journals. Overall, discrepancies were noted for 20 of the 33 evaluated trials. Differences in the apparent benefit reporting were found in 39% (24/61) pairwise comparisons and in 11 cases these were statistically significant; the FDA report showed greater benefit than the journal publication in 15 comparisons and lesser benefit in 9. Differences in the reported harms were found in 51% (28/55) pairwise comparisons and were statistically significant in 5. The "signal" in FDA reports showed a less harmful effect than the journal publication in 17 comparisons whereas a more harmful effect in 11. The differences were attributed to differences in analytic approach, patient inclusion, rounding effect, and counting discrepancies. However, no differences were categorized as critical.ConclusionThere was no empirical evidence to suggest biased estimates between the two sources. Increased and detailed transparency in publications would improve the understanding and credibility of published results. Further, the FDA report was found to be a useful source when data are missing in the published report (i.e. reporting bias).

Project description:BackgroundThe NCBI dbEST currently contains more than eight million human Expressed Sequenced Tags (ESTs). This wide collection represents an important source of information for gene expression studies, provided it can be inspected according to biologically relevant criteria. EST data can be browsed using different dedicated web resources, which allow to investigate library specific gene expression levels and to make comparisons among libraries, highlighting significant differences in gene expression. Nonetheless, no tool is available to examine distributions of quantitative EST collections in Gene Ontology (GO) categories, nor to retrieve information concerning library-dependent EST involvement in metabolic pathways. In this work we present the Human EST Ontology Explorer (HEOE) http://www.itb.cnr.it/ptp/human_est_explorer, a web facility for comparison of expression levels among libraries from several healthy and diseased tissues.ResultsThe HEOE provides library-dependent statistics on the distribution of sequences in the GO Direct Acyclic Graph (DAG) that can be browsed at each GO hierarchical level. The tool is based on large-scale BLAST annotation of EST sequences. Due to the huge number of input sequences, this BLAST analysis was performed with the aid of grid computing technology, which is particularly suitable to address data parallel task. Relying on the achieved annotation, library-specific distributions of ESTs in the GO Graph were inferred. A pathway-based search interface was also implemented, for a quick evaluation of the representation of libraries in metabolic pathways. EST processing steps were integrated in a semi-automatic procedure that relies on Perl scripts and stores results in a MySQL database. A PHP-based web interface offers the possibility to simultaneously visualize, retrieve and compare data from the different libraries. Statistically significant differences in GO categories among user selected libraries can also be computed.ConclusionThe HEOE provides an alternative and complementary way to inspect EST expression levels with respect to approaches currently offered by other resources. Furthermore, BLAST computation on the whole human EST dataset was a suitable test of grid scalability in the context of large-scale bioinformatics analysis. The HEOE currently comprises sequence analysis from 70 non-normalized libraries, representing a comprehensive overview on healthy and unhealthy tissues. As the analysis procedure can be easily applied to other libraries, the number of represented tissues is intended to increase.

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Crossmaps: visualization of overlapping relationships in collections of journal papers.

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