1,721,234 research outputs found
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
Multi-omics analysis of adipogenesis
Get PDFUnderstanding adipocyte development, also known as adipogenesis, is crit- ical for improving metabolic health. Additionally, adipose tissue depot, sex, age, body-mass index, Type 2 diabetes status, and cell size are known to play important roles in adipogenesis. Here, two classes of unsupervised learning methods were used to investigate which of these phenotypes were most strongly associated with differences in omics data between human adipocytes. Data consisted of two omics data types, including RNA- Sequencing (RNA-Seq) and Adipocyte Profiling (AP), as well as nine pa- tient phenotypes: day, depot, sex, age, body-mass index, Type 2 diabetes status, mean cell area, cell area variance, and % small cells. Data was clustered with a systematic pipeline using either single-view learning or multi-view learning methods, where single-view learning was performed using Principal Component Analysis (PCA) and multi-view learning was performed using either Multi-Omics Factor Analysis (MOFA+) or Mul- tiple Canonical Correlation Analysis (MCCA). Chi-square permutation tests were used to test for association between K-Means clusters and phe- notype labels and an extra layer of FDR-Correction was performed to account for multiple testing across all clustering analyses. Day (RNA-Seq PCA P-Value = 0.0092; AP PCA P-Value = 0.4224; MOFA+ P-Value = 0.0078; MCCA P-Value = 0.0092) and depot (RNA-Seq PCA P-Value = 0.0352; AP PCA P-Value = 0.0736; MOFA+ P-Value = 0.0450; MCCA P-Value = 0.0092) were identified as the primary drivers of clustering. However, none of the other phenotypes drove clustering, potentially due to unbalanced clusters or reduced statistical power from stratification. This multi-omics analysis of adipogenesis demonstrates the power of unsuper- vised clustering methods for the generation of robust biological insights that are consistent with the current literature surrounding day and depot. Nonetheless, since neither single-view nor multi-view learning methods de- tected associations for any of the other phenotypes, the inclusion of more samples and more omics is preferred for future studies
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
Gene set analysis for interpreting genetic studies
No full textInterpretation of genome-wide association study (GWAS) results is lacking behind the discovery of new genetic associations. Consequently, there is an urgent need for data-driven methods for interpreting genetic association studies. Gene set analysis (GSA) can identify aetiologic pathways and functional annotations and may hence point towards novel biological insights. However, despite the growing availability of GSA tools, the sizeable amount of variants identified for a vast number of complex traits, and many irrefutably trait-associated gene sets, the gap between discovery and interpretation remains. More efficient interpretation requires more complete and consistent gene set representations of biological pathways, phenotypes and functional annotations. In this review, I examine different types of gene sets, discuss how inconsistencies in gene set definitions impact GSA, describe how GSA has helped to elucidate biology and outline potential future directions.</p
Integrative Systems Biology: Elucidating Complex Traits
Get PDFRisk-phenotypes and diseases are oen caused by perturbed cellular networks, as biological processes depend on an overwhelming number of heavily intertwined components. e impact of a genetically altered gene may ripple through its molecular neighborhood instead of being confined to the gene product itself. My doctoral studies have been focused on the development of integrative approaches to identify systemic risk-modifying and disease-causing patterns. ey have been rooted in the hypothesis that data integration of complementary data sets may yield additional etiologic insights compared to analyses conducted within a single type of data. e first line of research presented here outlines two integrative methodologies designed to identify etiological pathways and susceptibility genes. In Paper I, my coworkers and I present an integrative approach that interrogates protein complexes for enrichment in incident coronary heart disease (CHD) associations from genome-wide association (GWA) data. We show that integration of a moderately powered GWA data with protein-protein interaction (PPI) data successfully identifies candidate susceptibility genes for incident CHD. In Paper II, we present an integrative method that combines heterogeneous data from GWA studies, PPI screens, disease similarities, linkage studies, and gene expression experiments into a multi-layered evidence network, which can be used to prioritize the protein-coding part of the genome according to a particular indication. We applied the method to bipolar disorder and type diabetes, and validated it by replicating a single-nucleotide polymorphism (SNP) within a novel bipolar disorder susceptibility gene. Next, I present the avenue of my research that has been focused on the analysis of genetic variation in obesity. In section ., I outline results from our bioinformaticsbased analysis of the FTO locus. Genetic variation within the FTO locus provides the hitherto strongest association between common SNPs and obesity, but the mechanisms leading to this association are still unknown. In Paper III, we demonstrate that body-mass index associated gene products coalesce onto distinct protein complexes, and show that these putative risk modules incriminate novel candidate obesitysusceptibility genes. e last overall line of research presented here, provides examples on how networks of human metabolism may serve as a data integration framework for differential gene expression data. In Paper IV, we present a method that can be used to identify metabolically-related sets of enzymes, which exhibit modest but concordant changes in gene expression. In Paper V, we used that approach to identify metabolites as biomarkers for weight maintenance upon dietary-induced weight loss. e approaches presented in this PhD esis provide integrative methodologies for the aggregation of multiple, functionally relevant data types. Together they represent a novel bioinformatics-based toolbox for analyses of genetic variation in human traits and disease. e esis is structured as follows. Chapter presents a few introductory remarks to integrative systems biology, and Chapter gives a brief description of human genetic variation and GWA analysis. Chapters - present the main topics in the esis (integrative methodologies for the analysis of GWA data, integrative analyses of genetic variation in obesity, and integrative analyses based on metabolic networks). Chapter summarizes the esis with a few concluding remarks
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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