1,721,168 research outputs found
La bioinformatica come strumento per lo studio dell'espressione genica durante lo sviluppo embrionale
Full text linkRegulation of gene expression during embryogenesis and development is a crucial cluefor a normal anatomy and physiology [1]. The combined use of experimental highthroughputmethods, such as DNA microarrays, and bioinformatic methods has innovatedthe analysis of temporal patterns of gene expression in embryos. Microarray analysis, infact, provides a large amount of data -at molecular level- that once acquired, must befunctionally integrated in order to find common patterns within a defined group of biologicalsamples.Through the use of cDNA microarrays, investigators can measure mRNA [1] levels forthousands of genes simultaneously, rather than one gene at a time. In fact, DNA microarraysare constituted by small glass or filter matrix that contain arrays of DNA sequences(each highly specific to a single gene) and by means hybridization of fluorescentcDNA, they are capable of simultaneously quantifying the expression of thousands ofgenes in a single experiment. The results of these experiments are spots whose brightnessvaries from gene to gene corresponding to the transcriptional activity of the examinedgenes (Fig. 1). This analysis requires sophisticated bioinformatics tool [2]. Aninterestingly application of DNA microarrays is the analysis of mRNA expression (suchas the transcriptome) in embryos. A systematic genomic approach to analyze globalgene expression patterns and functions during embryogenesis has recently been nameddevelopmental genomics or embryogenomics [3].Functional genomics of embryo development requires the integration of informationfrom genome sequence and structure, gene and protein expression, and metabolite profileswith knowledge databases by using computational and bioinformatics tools [1,4]
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
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