376 research outputs found
Generative discriminative models for multivariate inference and statistical mapping in medical imaging
This paper presents a general framework for obtaining interpretable
multivariate discriminative models that allow efficient statistical inference
for neuroimage analysis. The framework, termed generative discriminative
machine (GDM), augments discriminative models with a generative regularization
term. We demonstrate that the proposed formulation can be optimized in closed
form and in dual space, allowing efficient computation for high dimensional
neuroimaging datasets. Furthermore, we provide an analytic estimation of the
null distribution of the model parameters, which enables efficient statistical
inference and p-value computation without the need for permutation testing. We
compared the proposed method with both purely generative and discriminative
learning methods in two large structural magnetic resonance imaging (sMRI)
datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Using
the AD dataset, we demonstrated the ability of GDM to robustly handle
confounding variations. Using Schizophrenia dataset, we demonstrated the
ability of GDM to handle multi-site studies. Taken together, the results
underline the potential of the proposed approach for neuroimaging analyses.Comment: To appear in MICCAI 2018 proceeding
Moving Beyond Noninformative Priors: Why and How to Choose Weakly Informative Priors in Bayesian Analyses
Throughout the last two decades, Bayesian statistical methods have proliferated throughout ecology and evolution. Numerous previous references established both philosophical and computational guidelines for implementing Bayesian methods. However, protocols for incorporating prior information, the defining characteristic of Bayesian philosophy, are nearly nonexistent in the ecological literature. Here, I hope to encourage the use of weakly informative priors in ecology and evolution by providing a âconsumer\u27s guideâ to weakly informative priors. The first section outlines three reasons why ecologists should abandon noninformative priors: 1) common flat priors are not always noninformative, 2) noninformative priors provide the same result as simpler frequentist methods, and 3) noninformative priors suffer from the same high type I and type M error rates as frequentist methods. The second section provides a guide for implementing informative priors, wherein I detail convenient âreferenceâ prior distributions for common statistical models (i.e. regression, ANOVA, hierarchical models). I then use simulations to visually demonstrate how informative priors influence posterior parameter estimates. With the guidelines provided here, I hope to encourage the use of weakly informative priors for Bayesian analyses in ecology. Ecologists can and should debate the appropriate form of prior information, but should consider weakly informative priors as the new âdefaultâ prior for any Bayesian model
On the combination of omics data for prediction of binary outcomes
Enrichment of predictive models with new biomolecular markers is an important
task in high-dimensional omic applications. Increasingly, clinical studies
include several sets of such omics markers available for each patient,
measuring different levels of biological variation. As a result, one of the
main challenges in predictive research is the integration of different sources
of omic biomarkers for the prediction of health traits. We review several
approaches for the combination of omic markers in the context of binary outcome
prediction, all based on double cross-validation and regularized regression
models. We evaluate their performance in terms of calibration and
discrimination and we compare their performance with respect to single-omic
source predictions. We illustrate the methods through the analysis of two real
datasets. On the one hand, we consider the combination of two fractions of
proteomic mass spectrometry for the calibration of a diagnostic rule for the
detection of early-stage breast cancer. On the other hand, we consider
transcriptomics and metabolomics as predictors of obesity using data from the
Dietary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome
(DILGOM) study, a population-based cohort, from Finland
Quality management in heavy duty manufacturing industry: TQM vs. Six Sigma
âIs TQM a management fad?â This question has been extensively documented in the quality management literature; and will be tackled in this research though a critical literature review on the area. âTQM versus Six-Sigmaâ debate, which has also been a fundamental challenge in this research filed, is addressed by a thematic and chronological review on the peer papers. To evaluate this challenge in practice, a primary research in heavy duty machinery production industry have been conducted using a case-study on, J C Bamford Excavators Ltd (JCB), the largest European construction machinery producer. The result highlights that TQM is a natural foundation to build up Six-Sigma upon; and not surprisingly the quality yield in a TQM approach complemented by Six-sigma is far higher and more stable than when TQM with no Six-Sigma focus is being put in place; thus presenting the overall finding that TQM and Six Sigma are compliments, not substitutes. The study will be concluded with an overview on quality management approaches in the heavy duty manufacturing industry to highlight the way forward for the industry
Differential expression analysis with global network adjustment
<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a geneâs expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p>
<p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient âover-shrinkageâ method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p>
<p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p>
The geography of recent genetic ancestry across Europe
The recent genealogical history of human populations is a complex mosaic
formed by individual migration, large-scale population movements, and other
demographic events. Population genomics datasets can provide a window into this
recent history, as rare traces of recent shared genetic ancestry are detectable
due to long segments of shared genomic material. We make use of genomic data
for 2,257 Europeans (the POPRES dataset) to conduct one of the first surveys of
recent genealogical ancestry over the past three thousand years at a
continental scale. We detected 1.9 million shared genomic segments, and used
the lengths of these to infer the distribution of shared ancestors across time
and geography. We find that a pair of modern Europeans living in neighboring
populations share around 10-50 genetic common ancestors from the last 1500
years, and upwards of 500 genetic ancestors from the previous 1000 years. These
numbers drop off exponentially with geographic distance, but since genetic
ancestry is rare, individuals from opposite ends of Europe are still expected
to share millions of common genealogical ancestors over the last 1000 years.
There is substantial regional variation in the number of shared genetic
ancestors: especially high numbers of common ancestors between many eastern
populations likely date to the Slavic and/or Hunnic expansions, while much
lower levels of common ancestry in the Italian and Iberian peninsulas may
indicate weaker demographic effects of Germanic expansions into these areas
and/or more stably structured populations. Recent shared ancestry in modern
Europeans is ubiquitous, and clearly shows the impact of both small-scale
migration and large historical events. Population genomic datasets have
considerable power to uncover recent demographic history, and will allow a much
fuller picture of the close genealogical kinship of individuals across the
world.Comment: Full size figures available from
http://www.eve.ucdavis.edu/~plralph/research.html; or html version at
http://ralphlab.usc.edu/ibd/ibd-paper/ibd-writeup.xhtm
Do we (seem to) perceive passage?
I examine some recent claims put forward by L. A. Paul, Barry Dainton and Simon Prosser, to the effect that perceptual experiences of movement and change involve an (apparent) experience of âpassageâ, in the sense at issue in debates about the metaphysics of time. Paul, Dainton and Prosser all argue that this supposed feature of perceptual experience â call it a phenomenology of passage â is illusory, thereby defending the view that there is no such a thing as passage, conceived of as a feature of mind-independent reality. I suggest that in fact there is no such phenomenology of passage in the first place. There is, however, a specific structural aspect of the phenomenology of perceptual experiences of movement and change that can explain how one might mistakenly come to the belief that such experiences do involve a phenomenology of passage
Real-time traffic event detection using Twitter data
Incident detection is an important component of intelligent transport systems and plays a key role in urban traffic management and provision of traveller information services. Due to its importance, a wide number of researchers have developed different algorithms for real-time incident detection. However, the main limitation of existing techniques is that they do not work well in conditions where random factors could influence traffic flows. Twitter is a valuable source of information as its users post events as they happen or shortly after. Therefore, Twitter data have been used to predict a wide variety of real-time outcomes. This paper aims to present a methodology for a real-time traffic event detection using Twitter. Tweets are obtained through the Twitter streaming application programming interface in real time with a geolocation filter. Then, the author used natural language processing techniques to process the tweets before they are fed into a text classification algorithm that identifies if it is traffic related or not. The authors implemented their methodology in the West Midlands region in the UK and obtained an overall accuracy of 92·86%
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The use of the Kalman filter in the automated segmentation of EIT lung images
In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging
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