135 research outputs found
Robust variable selection through MAVE
No full textDimension reduction and variable selection play important roles in high dimensional data analysis. The sparse MAVE, a model-free variable selection method, is a nice combination of shrinkage estimation, Lasso, and an effective dimension reduction method, MAVE (minimum average variance estimation). However, it is not robust to outliers in the dependent variable because of the use of least-squares criterion. A robust variable selection method based on sparse MAVE is developed, together with an efficient estimation algorithm to enhance its practical applicability. In addition, a robust cross-validation is also proposed to select the structural dimension. The effectiveness of the new approach is verified through simulation studies and a real data analysis
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Mapping MAVE data for use in human genomics applications
No full textBackgroundExperimental data from functional assays have a critical role in interpreting the impact of genetic variants. Assay data must be unambiguously mapped to a reference genome to make it accessible, but it is often reported relative to assay-specific sequences, complicating downstream use and integration of variant data across resources. To make multiplexed assays of variant effect (MAVE) data more broadly available to the research and clinical communities, the Atlas of Variant Effects Alliance mapped MAVE data from the MaveDB community database to human reference sequences, creating an extensive set of machine-readable homology mappings that are incorporated into widely used human genomics applications.
ResultsHere, we map approximately 9.0 million individual protein and nucleotide variants in MaveDB to the human genome, describing the examined variants with respect to human reference sequences while preserving the data provenance of the original MAVE sequences. We then disseminate the results to major genomic resources including the Genomics 2 Proteins Portal, UCSC Genome Browser, Ensembl Variant Effect Predictor, and DECIPHER platform. Within these applications, MAVE variants can now be visualized and integrated with other relevant clinical and biological data, making additional knowledge available when performing variant interpretation and conducting other research activities.ConclusionsMapping MAVE variants to human reference sequences and sharing the mapped dataset with several key human genomics applications enables a new and diverse set of applications for MAVE data. This study provides increased access to functional data that can assist in clinical variant interpretation pipelines and enable biomedical research and discovery
The Project of Restructure in Organization CTCenter MaVe s.r.o.
No full textDiplomová práce popisuje návrh řešení personální situace v centru klinických hodnocení CTCenter MaVe s. r. o. Práce byla rozdělena na část teoretickou a praktickou, přičemž teoretická část se věnuje problematice personálního řízení a specifikům personálního řízení ve zdravotnictví. Praktická část analyzuje prostředí organizace. Na základě analýz a zejména personální analýzy byl navržen systém opatření k zavedení optimálního personálního obsazení centra. Centrum klinických hodnocení je velice specifickým typem zdravotnického zařízení, proto muselo být i při tvorbě tohoto projektu postupováno ne zcela obvyklým způsobem. Projekt byl navržen společnosti k zavedení jako efektivní a funkční.The thesis describes a solution design of personnel situation in the clinical trials center CTCenter MaVe s. r. o. The thesis is divided into theoretical and practical sections. While the theoretical part deals with the personnel management and with specifics of personal management in the healthcare, practical section analyzes the environment of the organisa-tion. On the basis of this analysis especially analysis of the personnel situation a system of measures to be implemented for achievement of optimal personnel staffing was suggest-ed. The clinical trials centre is a very specific type of medical facility therefore the project itself had to be carried out in an uncommon manner. The project was suggested to the company for implementation as effective and functional.Ústav managementu a marketing
Penalized single-index quantile regression
No full textThis article is made available through the Brunel Open Access Publishing Fund. Copyright for this article is retained by the author(s), with first publication rights granted to the journal.
This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution
license (http://creativecommons.org/licenses/by/3.0/).The single-index (SI) regression and single-index quantile (SIQ) estimation methods product linear combinations of all the original predictors. However, it is possible that there are many unimportant predictors within the original predictors. Thus, the precision of parameter estimation as well as the accuracy of prediction will be effected by the existence of those unimportant predictors when the previous methods are used. In this article, an extension of the SIQ method of Wu et al. (2010) has been proposed, which considers Lasso and Adaptive Lasso for estimation and variable selection. Computational algorithms have been developed in order to calculate the penalized SIQ estimates. A simulation study and a real data application have been used to assess the performance of the methods under consideration
jonas-fuchs/virHEAT: v.0.7
No full text<h2>Update virHEAT to 0.7</h2>
<h4>New</h4>
<ul>
<li>added <code>-s --scores</code> option that lets you link results from <a href="https://mavedb.org/">deep mutational screens</a> to the heatmap</li>
<li>added some more error catching</li>
<li>added MAVE example data</li>
<li>updated documentation</li>
</ul>
<p></p>
<h2>New Contributors</h2>
<ul>
<li>@PlushZ made their first contribution in https://github.com/jonas-fuchs/virHEAT/pull/13
Thanks a lot! :100:</li>
</ul>
<p><strong>Full Changelog</strong>: https://github.com/jonas-fuchs/virHEAT/compare/v.0.6...v.0.7</p>
Clustering in dimension reduction for function approximation problem
No full textIn order to reduce the dimension of input vectors before construction of ap-proximation MAVE-type methods (minimum average variance estimation) can be used, however they are very computationally intensive. In the present work the modification of method MAVE is described which allows substantial decrease of algorithm run time at the expense of small error increase
Clustering in dimension reduction for function approximation problem
No full textIn order to reduce the dimension of input vectors before construction of ap-proximation MAVE-type methods (minimum average variance estimation) can be used, however they are very computationally intensive. In the present work the modification of method MAVE is described which allows substantial decrease of algorithm run time at the expense of small error increase
AMČR - archeologický záznam C-201441997A
No full textStav: 3Označení: ZAV 2014-09; ARÚ Praha 147/2016Lokalizace/okolnosti: V areálu drůbežárny Mave Jičín a.s.; parc. č. 66/3. Výkop pro vodovod v původní trase a profilu
Ramsløg og tyttebær kan måske erstatte antibiotika
No full textArtiklen er viderebragt fra ICROFS pressemeddelelsen v. Canibe og Jensen,
Beskriver in vivo pilot forsøg med ramsløg og tyttebær til grise der sænker E coli i mave-tarm på fravænningsgrise
AMČR - archeologický záznam C-9125423A
No full textStav: 3Označení: ARÚ Praha 5656/1996Lokalizace/okolnosti: Mezi obcí Soběraz a drůbežárnou firmy MAVE Soběraz.Souhrn/upřesnění: Sídliště známé od počátku 20. stol. zachyceno i při letecké prospekci 21. 6. 1995. V M Jičín a v M Železnice jsou uloženy četné nálezy keramiky a nástrojů z této lokality
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