1,721,035 research outputs found
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
Statistical Methods for the Analysis of Autosomal and X Chromosome Genetic Data in Samples with Unknown Structure
Full text linkThesis (Ph.D.)--University of Washington, 2016-03Genome-wide association studies (GWAS) and sequencing association studies are routinely conducted for the mapping of genes to complex traits. Genetic variants on the X chromosome could potentially play an important role in some complex traits, however, statistical methods for association studies have primarily been developed for variants on the autosomal chromosomes with significantly less attention given to the X chromosome. Existing association methods for variants on the autosomal chromosomes will typically not be valid for the analysis of X-linked variants due to the X chromosome having a different correlation structure than the autosomes as well as copy number differences for males and females on the X. This dissertation develops and applies new statistical methodology for genetic analysis of variants on the X chromosome. In particular, we focus on methods that are computationally feasible for large-scale genomic data for detecting genetic associations with common and rare variants from GWAS and sequencing studies. Furthermore, the proposed methods allow for valid genetic analysis in the presence of complex sample structures, such as population structure and cryptic relatedness among sampled individuals. Another aspect of this dissertation is the development of statistical methods for inference of heterogeneity in ancestry across the genome (including the X chromosome) in recently admixed populations, such as African Americans and Hispanics, who have experienced admixing within the past few hundred years from two or more continental groups that were previously isolated
A more powerful quasi-likelihood score test for detecting genetic association with multivariate phenotypes in related samples
Full text linkThesis (Master's)--University of Washington, 2015Pleiotropy is a commonly observed phenomenon in human genetics where a single gene influences multiple, and sometimes seemingly unrelated traits. Recently there has been significant interest in the identification of genetic variants that are associated with multiple phenotypes since identifying pleiotropic effects can lead to a better understanding of the underpinnings of complex traits. Genome-wide association studies often collect data on a variety of phenotypes, and a number of methods have been proposed for the joint analysis of multiple phenotypes in unrelated samples. Many genetic studies, however, include related individuals. In this thesis, we consider the problem of genetic association testing with multivariate phenotypes in samples with relatedness. We propose the multivariate phenotype quasi-likelihood (MPQ) score test for association mapping in related samples. Some of the features of the MPQ are: (1) it is applicable to completely general combinations of family and population-based samples, (2) it allows for the analysis of general quantitative traits and can accommodate both binary and continuous outcomes, (3) it can incorporate information on covariates in the analysis, and (4) it is computationally feasible for large-scale GWAS allowing for arbitrary relatedness among sample individuals. In simulation studies with unrelated and related samples, we demonstrate that the MPQ represents an overall, and in many cases, substantial, improvement, over existing multivariate methods, in terms of type-1 error rate and power, for a variety of causal models and multivariate trait correlation structures. Finally, we apply the MPQ test to a GWAS of 3,548 Hispanic American postmenopausal women from the Women’s Health Initiative SNP Health Association Resource to identify genetic variants associated with pleiotropic effects on serum C-reactive peptide (CRP) and white blood cell counts (WBC), two inflammation-related phenotypes. The MPQ test identifies previously reported variants for CRP and WBC as well as novel variants that are genome-wide significant
Statistical Methods for Inferring Population Structure with Human Genome Sequence Data
No full textThesis (Ph.D.)--University of Washington, 2016-12Population structure is systematic variation in the human genome due to non-random mating because of physical or cultural barriers. Population structure is of interest in several fields of medicine, including population genetics, medical genetics, and personalized genomics. Advances in sequencing technology have lead to a precipitous drop in the cost to sequence the human genome, which has lead to a plethora of sequencing studies in recent years. This increase in the availability of genotype data has led to a commensurate increase in the number of statistical methods for analyzing sequence data. To date, the majority of these new methods have focused on association testing, with relatively little work on inferring population structure, despite the importance of population structure inference. There are several challenges to inferring population structure with sequencing data, including: an abundance of rare variants (loci where there is little variation across human populations) and the large number of loci. Existing methods are not directly applicable to rare variants and few computationally feasible methods exist. This dissertation considers the problem of inferring population structure with human genome sequence data. We present new statistical methods, with theoretical justification, extensive simulation studies, and applications to the 1000 Genomes Project data. We also develop extensions of the methods that are computationally feasible for large sequencing data sets and that allow for the use of reference population samples to better elucidate population structure from sequence data
Prediction of CYP3A4 metabolic activity from whole genome RNA-seq data with feature selection machine learning methods
No full textThesis (Master's)--University of Washington, 2017-08CYP3A4, one of the isozyme of the cytochromes P450 (CYPs), contributes significantly to drug clearance and drug-drug interactions. The goals of this project are to identify hepatically-expressed genes that are associated with CYP3A4 metabolic activity in human liver tissue and to predict CYP3A4 activity using gene expression data from whole genome RNA sequences. Due to the high-dimensionality of the dataset, we applied lasso and elastic net, two feature selection machine learning methods, for prediction and graphical lasso was used for constructing gene network graphs. A simulation study was performed to assess the performance of the prediction algorithms and to evaluate the efficiency of gene selection using the machine learning methods. We assessed prediction performance based on correlations, and the correlation between measured CYP3A4 activity and predicted activity was approximately 0.4 and 0.5 when reductase was excluded and included, respectively, for both lasso and elastic net. In addition to the CYP3A4 gene, we also identified the GZMA gene as a strong candidate for prediction of CYP3A4 activity that should be investigated in future studies
Validation of single nucleotide polymorphisms associated with acute kidney injury in bone marrow transplant recipients
No full textThesis (Master's)--University of Washington, 2013Validation of single nucleotide polymorphisms associated with acute kidney injury in bone marrow transplant recipients Laurel K. Willig Timothy Thornton Background: Rates of acute kidney injury (AKI) as high as 75% have been reported in hematopoietic stem cell transplant (HSCT) patients. Previous studies have identified single nucleotide polymorphisms (SNP) associated with AKI. However, few studies, have examined genetic associations with AKI in HSCT recipients. Methods: We perform a case-control analysis in a sample of 795 HSCT recipients who developed AKI and 1052 HSCT recipients without AKI. 18 SNPs that were previously identified as candidates for AKI were tested for association using multivariate logistic regression models. Results: We identified three genetic polymorphisms associated with developing AKI. The T allele in rs3024495 and the C allele in rs1800896 of interleukin 10 (IL-10) both decreased the odds of developing AKI (OR=0.75, p=0.0029 and OR=0.84, p=0.011 respectively). The C allele for rs4540055 in toll-like receptor 1 (TLR1) increased the odds of developing AKI (OR=1.84, p=0.0016). Both rs3034495 and rs4540055 remained significant after adjustment for covariates. Conclusions: Genetic polymorphisms in IL-10 and TLR1 were associated with AKI development in HSCT recipients
Statistical Methods for Transcriptome-Wide Association Studies in Ancestrally Diverse Populations
Full text linkThesis (Ph.D.)--University of Washington, 2022Transcriptome-wide association studies (TWAS) have become more commonly used in recent years. TWAS integrate genome-wide association studies (GWAS) with gene expression mapping studies in order to identify genes whose gene expression is associated with the phenotype. The main goals of TWAS are in providing insights into biological mechanisms underlying disease etiology and in helping interpret the results of GWAS. TWAS conducted in large-scale ancestrally diverse cohorts face multiple challenges, including the presence of population structure, known or cryptic relatedness and heterogeneity in phenotypic distributions across subgroups. There is a dearth of statistical methodology available to researchers that addresses the aforementioned issues. In this dissertation, we evaluate the performance of existing TWAS methods in ancestrally diverse populations and identify their limitations. We then develop new statistical methodology that addresses these limitations. We validate the performance of the novel methods in extensive series of simulations as well as in applications to large cohorts of ancestrally diverse populations from the Trans-Omics for Precision Medicine (TOPMed) program
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