142 research outputs found
Applications and extensions of Random Forests in genetic and environmental studies
No full textTranscriptional regulation refers to the molecular systems that control the concentration of mRNA species within the cell. Variation in these controlling systems is not only responsible for many diseases, but also contributes to the vast phenotypic diversity in the biological world. There are powerful experimental approaches to probe these regulatory systems, and the focus of my doctoral research has been to develop and apply effective computational methods that exploit these rich data sets more completely. First, I present a method for mapping genetic regulators of gene expression (expression quantitative trait loci, or eQTL) using Random Forests. This approach allows for flexible modeling and feature selection, and results in eQTL that are more biologically supportable than those mapped with competing methods. Next, I present a method that finds interactions between genes that in turn regulate the expression of other genes. This is accomplished by finding recurring decision motifs in the forest structure that represent dependencies between genetic loci. Third, I present a method to use distributional differences in eQTL data to establish the regulatory roles of genes relative to other disease-associated genes. Using this method, we found that genes that are master regulators of other disease genes are more likely to be consistently associated with the disease in genetic association studies. Finally, I present a novel application of Random Forests to determine the mode of regulation of toxin-perturbed genes, using time-resolved gene expression. The results demonstrate a novel approach to supervised weighted clustering of gene expression data
The effectiveness of the stylometry of function words in discriminating between Shakespeare and Fletcher
No full textA number of recent successful authorship studies have relied on a statistical
analysis of language features based on function words. However, stylometry has
not been extensively applied to Elizabethan and Jacobean dramatic questions.
To determine the effectiveness of such an approach in this field, language features
are studied in twenty-four plays by Shakespeare and eight by Fletcher. The goal
is to develop procedures that might be used to determine the authorship of
individual scenes in The Two Noble Kinsmen and Henry VIII.
Homonyms, spelling variants and contracted forms in old-spelling dramatic
texts present problems for a computer analysis. A program that uses a system of
pre-edit codes and replacement /expansion lists was developed to prepare versions
of the texts in which all forms of common words can be recognized automatically.
To evaluate some procedures for determining authorship developed by A. Q.
Morton and his colleagues, occurrences of 30 common collocations and 5 proportional
pairs are analyzed in the texts. Within-author variation for these features
is greater than had been found in previous studies. Univariate chi-square tests
are shown to be of limited usefulness because of the statistical distribution of
these textual features and correlation between pairs of features. The best of the
collocations do not discriminate as well as most of the individual words from
which they are composed.
Turning to the rate of occurrence of individual words and groups of words, distinctiveness
ratios and t-tests are used to select variables that best discriminate
between Shakespeare and Fletcher. Variation due to date of composition and
genre within the Shakespeare texts is examined. A multivariate and distributionfree
discriminant analysis procedure (using kernel estimation) is introduced. The
classifiers based on the best marker words and the kernel method are not greatly
affected by characterization and perform well for samples as short as 500 words.
When the final procedure is used to assign the 459 scenes of known authorship
(containing at least 500 words)almost 112 95% are assigned to the correct author. Only
two scenes are incorrectly classified, and 4.8% of the scenes cannot be assigned
to either author by the procedure. When applied to individual scenes of at least 500 words in The Two Noble
Kinsmen and Henry VIII, the procedure indicates that both plays are collaborations
and generally supports the usual division. However, the marker words in
a number of scenes often attributed to Fletcher are very much closer to Shakespeare's
pattern of use. These scenes include TNK IV.iii and H8 I.iii, IV.i-ii
and V.iv
Mark Twain: Mysterious stranger
No full textOne hundred years after his death, the curators of this exhibition in The Rare Book & Manuscript Library, have explored the enormous holdings of the Library to assemble and present this glimpse of Mark Twain the author, publisher, erstwhile tycoon, and world-wide celebrity … and Sam Clemens the husband, the father, and the friend.not peer reviewedSubmitted by Dennis Sears ([email protected]) on 2010-06-02T16:17:26Z
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Previous issue date: 2010-04-16published or submitted for publicatio
Investigation of e-textile dipole antenna performance based on embroidery parameters
No full textE-textile antennas have the potential to be the premier on-body wearable sensor. Embroidery techniques, which can be applied to produce e-textile antennas, assist in large production volumes and fast production speeds. This paper focuses on the effects of three commonly used embroidery parameters, namely stitch type, conductive thread location, and stabilizer, on the performance of embroidered dipole antennas in order to determine the ideal embroidery combination for optimal antenna performance. Fifty-four dipole antenna samples were fabricated and measured at the industrial, scientific, and medical (ISM) frequency band of 2.45 GHz. The results of this study show that machine-embroidered antenna designs with satin stitches resonate at a lower frequency and exhibit a lower transmission gain compared with those made with contour stiches, and the conductive thread location in the bobbin location plus the use of a water-soluble stabilizer can help improve impedance matching.This accepted article is published Agu D, Eike RJ, Cliett A, Michaelson D, Cloud R, Li Y. Investigation of e-textile dipole antenna performance based on embroidery parameters. Textile Research Journal. 2022;92(15-16):2771-2783. doi:10.1177/00405175211013421. Posted with permission. © The Author(s) 2021<br
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The Law of the Lab: Using Zerit to Inform Technology Transfer
No full textThe author takes a comprehensive look at the government’s policy of technology transfer, the process by which government-funded inventions are transferred to the private sector for commercialization. Emphasis is placed on biomedical R&D and, in particular, on pharmaceutical drugs. The author describes how the Bayh-Dole Act of 1980 fundamentally altered the government’s approach to tech transfer. He explains why the Act is viewed as a success, and presents the three most significant current debates over the policy’s future. These debates, the author argues, can be informed by careful study of how tech transfer operates in practice. For this reason, the author then presents, in narrative form, the development of Zerit, an effective AIDS medication supported by government funding and then transferred to Bristol-Myers Squibb for commercialization. This narrative is then used by the author to inform the three current debates over tech transfer. The author finds that the Bayh-Dole Act is generally successful, but that the government should do more to ensure that taxpayers are maximizing the return on their investment. The author also finds that the government should take greater care to consider not only international trade, but also public health, national security and ethical responsibility, when deciding whether to exercise government rights to taxpayer-funded inventions
TiSAn: Estimating Tissue Specific Effects of Coding and Noncoding Variants
No full textAbstractMeasures of general deleteriousness, like CADD or PolyPhen, have become indispensable tools in the interpretation of genetic variants. However, these measures say little about where in the organism these deleterious effects will be most apparent. An additional, complementary measure is needed to link deleterious variants (as determined by e.g., CADD) to tissues in which their effect will be most meaningful. Here, we introduce TiSAn (Tissue Specific Annotation), a tool that predicts how related a genomic position is to a given tissue (http://github.com/kevinVervier/TiSAn). TiSAn uses machine learning on genome-scale, tissue-specific data to discriminate variants relevant to a tissue from those having no bearing on the development or function of that tissue. Predictions are then made genome-wide, and these scores can then be used to contextualize and filter variants of interest in whole genome sequencing or genome wide association studies (GWAS). We demonstrate the accuracy and versatility of TiSAn by introducing predictive models for human heart and human brain, and detecting tissue-relevant variations in large cohorts for autism spectrum disorder (TiSAn-brain) and coronary artery disease (TiSAn-heart). We find that TiSAn is better able to prioritize genetic variants according to their tissue-specific action than the current state of the art method, GenoSkyLine.</jats:p
TiSAn: estimating tissue-specific effects of coding and non-coding variants
No full textAbstract
Motivation
Model-based estimates of general deleteriousness, like CADD, DANN or PolyPhen, have become indispensable tools in the interpretation of genetic variants. However, these approaches say little about the tissues in which the effects of deleterious variants will be most meaningful. Tissue-specific annotations have been recently inferred for dozens of tissues/cell types from large collections of cross-tissue epigenomic data, and have demonstrated sensitivity in predicting affected tissues in complex traits. It remains unclear, however, whether including additional genome-scale data specific to the tissue of interest would appreciably improve functional annotations.
Results
Herein, we introduce TiSAn, a tool that integrates multiple genome-scale data sources, defined by expert knowledge. TiSAn uses machine learning to discriminate variants relevant to a tissue from those with no bearing on the function of that tissue. Predictions are made genome-wide, and can be used to contextualize and filter variants of interest in whole genome sequencing or genome-wide association studies. We demonstrate the accuracy and flexibility of TiSAn by producing predictive models for human heart and brain, and detecting tissue-relevant variations in large cohorts for autism spectrum disorder (TiSAn-brain) and coronary artery disease (TiSAn-heart). We find the multiomics TiSAn model is better able to prioritize genetic variants according to their tissue-specific action than the current state-of-the-art method, GenoSkyLine.
Availability and implementation
Software and vignettes are available at http://github.com/kevinVervier/TiSAn.
Supplementary information
Supplementary data are available at Bioinformatics online.
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