1,721,039 research outputs found
The winner's curse under dependence: repairing empirical Bayes using convoluted densities
No full textThe winner's curse is a form of selection bias that arises when estimates are obtained for a large number of features, but only a subset of most extreme estimates is reported. It occurs in large scale significance testing as well as in rank-based selection, and imperils reproducibility of findings and follow-up study design. Several methods correcting for this selection bias have been proposed, but questions remain on their susceptibility to dependence between features since theoretical analyses and comparative studies are few. We prove that estimation through Tweedie's formula is biased in presence of strong dependence, and propose a convolution of its density estimator to restore its competitive performance, which also aids other empirical Bayes methods. Furthermore, we perform a comprehensive simulation study comparing different classes of winner's curse correction methods for point estimates as well as confidence intervals under dependence. We find a bootstrap method and empirical Bayes methods with density convolution to perform best at correcting the selection bias, although this correction generally does not improve the feature ranking. Finally, we apply the methods to a comparison of single-feature versus multi-feature prediction models in predicting Brassica napus phenotypes from gene expression data, demonstrating that the superiority of the best single-feature model may be illusory.FUND ING
The work of S.H. in the lab of S.M. was supported by Inari Agriculture NV, funded in part by VLAIO, grant HBC.2019.2814.
We thank Erik van Zwet for pointing us to the work by Azriel and Schwartzman (2015), and for useful discussions
Single-plant omics : a new methodology to gather knowledge in field conditions
No full textOm het probleem van klimaatverandering aan te pakken is het cruciaal om gewassen met een hogere stressbestendigheid en opbrengst te ontwikkelen. Een aanzienlijke uitdaging in dit streven is echter de kloof tussen laboratorium- en veldomstandigheden. Deze kloof ontstaat wanneer studies uitgevoerd onder gecontroleerde laboratoriumomstandigheden beperkte voorspellende waarde hebben voor fenotypes in reële veldomstandigheden. Om dit obstakel te overwinnen hebben we een nieuw experimenteel design geïmplementeerd, waarin we -omics en fenotype-profilering toepassen op individuele planten, met dezelfde genetische achtergrond en gegroeid in hetzelfde veld, om de moleculaire en fenotypische reacties van planten op veldomstandigheden te bestuderen. Door gebruik te maken van ongecontroleerde variaties in micro-omgeving en stochastische variaties in het veld kunnen we onderzoeken welke genen stressbestendigheid en opbrengst-fenotypes in het veld beïnvloeden. Deze methodologie werd getest op twee gewassen: maïs en koolzaad
Deciphering the evolutionary impact of gen(om)e duplications through mechanistic modeling of the genotype-phenotype map
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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
Variations on the Author
Full text link“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
Full text linkWe 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
Full text linkWe 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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