Institution
Vanderbilt University
Education•Nashville, Tennessee, United States•
About: Vanderbilt University is a education organization based out in Nashville, Tennessee, United States. It is known for research contribution in the topics: Population & Cancer. The organization has 45066 authors who have published 106528 publications receiving 5435039 citations. The organization is also known as: Vandy.
Topics: Population, Cancer, Poison control, Breast cancer, Receptor
Papers published on a yearly basis
Papers
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Ghent University1, University of California, San Diego2, Leiden University3, Dresden University of Technology4, Stanford University5, University of Maryland, College Park6, Indiana University7, University of Cambridge8, Cardiff University9, University of Western Ontario10, Monash University, Clayton campus11, University of Toronto12, University of Vermont13, University of Oregon14, University of Tasmania15, University of Oslo16, Utrecht University17, Katholieke Universiteit Leuven18, Yale University19, Vanderbilt University20, University of Amsterdam21, Anglia Ruskin University22, Indian Institute of Science23, Queen's University24, King's College London25, Michigan State University26, University of Iowa27, Trinity College, Dublin28
TL;DR: The goal is to facilitate a more accurate use of the stop-signal task and provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.
Abstract: Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.
617 citations
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TL;DR: The use of factor analysis as a method for examining the dimensional structure of data is contrasted with its frequent misapplication as a tool for identifying clusters and segments.
Abstract: The use of factor analysis as a method for examining the dimensional structure of data is contrasted with its frequent misapplication as a tool for identifying clusters and segments. Procedures for...
617 citations
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TL;DR: The authors developed and evaluated a measure called the Dimensional Obsessive-Compulsive Scale (DOCS) to address limitations of existing OC symptom measures and hold promise as a measure of OC symptoms in clinical and research settings.
Abstract: Although several measures of obsessive-compulsive (OC) symptoms exist, most are limited in that they are not consistent with the most recent empirical findings on the nature and dimensional structure of obsessions and compulsions. In the present research, the authors developed and evaluated a measure called the Dimensional Obsessive-Compulsive Scale (DOCS) to address limitations of existing OC symptom measures. The DOCS is a 20-item measure that assesses the four dimensions of OC symptoms most reliably replicated in previous structural research. Factorial validity of the DOCS was supported by exploratory and confirmatory factor analyses of 3 samples, including individuals with OC disorder, those with other anxiety disorders, and nonclinical individuals. Scores on the DOCS displayed good performance on indices of reliability and validity, as well as sensitivity to treatment and diagnostic sensitivity, and hold promise as a measure of OC symptoms in clinical and research settings.
617 citations
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TL;DR: Both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.
Abstract: Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.
616 citations
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TL;DR: The results suggest that the EGF-stimulated formation of inositol 1,4,5-trisphosphate and diacylglycerol in intact cells results, at least in part, from catalytic activation of PLC-gamma 1 through tyrosine phosphorylation.
Abstract: Phospholipase C-gamma 1 (PLC-gamma 1), an isozyme of the phosphoinositide-specific phospholipase C family, which occupies a central role in hormonal signal transduction pathways, is an excellent substrate for the epidermal growth factor (EGF) receptor tyrosine kinase. Epidermal growth factor elicits tyrosine phosphorylation of PLC-gamma 1 and phosphatidylinositol 4,5-bisphosphate hydrolysis in various cell lines. The ability of tyrosine phosphorylation to activate the catalytic activity of PLC-gamma 1 was tested. Tyrosine phosphorylation in intact cells or in vitro increased the catalytic activity of PLC-gamma 1. Also, treatment of EGF-activated PLC-gamma 1 with a tyrosine-specific phosphatase substantially decreased the catalytic activity of PLC-gamma 1. These results suggest that the EGF-stimulated formation of inositol 1,4,5-trisphosphate and diacylglycerol in intact cells results, at least in part, from catalytic activation of PLC-gamma 1 through tyrosine phosphorylation.
615 citations
Authors
Showing all 45403 results
Name | H-index | Papers | Citations |
---|---|---|---|
Walter C. Willett | 334 | 2399 | 413322 |
Meir J. Stampfer | 277 | 1414 | 283776 |
John Q. Trojanowski | 226 | 1467 | 213948 |
Robert M. Califf | 196 | 1561 | 167961 |
Matthew Meyerson | 194 | 553 | 243726 |
Scott M. Grundy | 187 | 841 | 231821 |
Tony Hunter | 175 | 593 | 124726 |
David R. Jacobs | 165 | 1262 | 113892 |
Donald E. Ingber | 164 | 610 | 100682 |
L. Joseph Melton | 161 | 531 | 97861 |
Ralph A. DeFronzo | 160 | 759 | 132993 |
David W. Bates | 159 | 1239 | 116698 |
Charles N. Serhan | 158 | 728 | 84810 |
David Cella | 156 | 1258 | 106402 |
Jay Hauser | 155 | 2145 | 132683 |