118 research outputs found
A minimum Wasserstein distance approach to Fisher's combination of independent discrete p-values
This paper introduces a comprehensive framework to adjust a discrete test
statistic for improving its hypothesis testing procedure. The adjustment
minimizes the Wasserstein distance to a null-approximating continuous
distribution, tackling some fundamental challenges inherent in combining
statistical significances derived from discrete distributions. The related
theory justifies Lancaster's mid-p and mean-value chi-squared statistics for
Fisher's combination as special cases. However, in order to counter the
conservative nature of Lancaster's testing procedures, we propose an updated
null-approximating distribution. It is achieved by further minimizing the
Wasserstein distance to the adjusted statistics within a proper distribution
family. Specifically, in the context of Fisher's combination, we propose an
optimal gamma distribution as a substitute for the traditionally used
chi-squared distribution. This new approach yields an asymptotically consistent
test that significantly improves type I error control and enhances statistical
power
Concussion classification via deep learning using whole-brain white matter fiber strains
Developing an accurate and reliable injury predictor is central to the
biomechanical studies of traumatic brain injury. State-of-the-art efforts
continue to rely on empirical, scalar metrics based on kinematics or
model-estimated tissue responses explicitly pre-defined in a specific brain
region of interest. They could suffer from loss of information. A single
training dataset has also been used to evaluate performance but without
cross-validation. In this study, we developed a deep learning approach for
concussion classification using implicit features of the entire voxel-wise
white matter fiber strains. Using reconstructed American National Football
League (NFL) injury cases, leave-one-out cross-validation was employed to
objectively compare injury prediction performances against two baseline machine
learning classifiers (support vector machine (SVM) and random forest (RF)) and
four scalar metrics via univariate logistic regression (Brain Injury Criterion
(BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the
corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based deep
learning and machine learning classifiers consistently outperformed all scalar
injury metrics across all performance categories in cross-validation (e.g.,
average accuracy of 0.844 vs. 0.746, and average area under the receiver
operating curve (AUC) of 0.873 vs. 0.769, respectively, based on the testing
dataset). Nevertheless, deep learning achieved the best cross-validation
accuracy, sensitivity, and AUC (e.g., accuracy of 0.862 vs. 0.828 and 0.842 for
SVM and RF, respectively). These findings demonstrate the superior performances
of deep learning in concussion prediction, and suggest its promise for future
applications in biomechanical investigations of traumatic brain injury.Comment: 18 pages, 7 figures, and 4 table
Two-stage joint selection method to identify candidate markers from genome-wide association studies
The interaction among multiple genes and environmental factors can affect an individual's susceptibility to disease. Some genes may not show strong marginal associations when they affect disease risk through interactions with other genes. As a result, these genes may not be identified by single-marker methods that are widely used in genome-wide association studies. To explore this possibility in real data, we carried out a two-stage model selection procedure of joint single-nucleotide polymorphism (SNP) analysis to detect genes associated with rheumatoid arthritis (RA) using Genetic Analysis Workshop 16 genome-wide association study data. In the first stage, the genetic markers were screened through an exhaustive two-dimensional search, through which promising SNP and SNP pairs were identified. Then, LASSO was used to choose putative SNPs from the candidates identified in the first stage. We then use the RA data collected by the Wellcome Trust Case Control Consortium to validate the putative genetic factors. Balancing computational load and statistical power, this method detects joint effects that may fail to emerge from single-marker analysis. Based on our proposed approach, we not only replicated the identification of important RA risk genes, but also found novel genes and their epistatic effects on RA. To our knowledge, this is the first two-dimensional scan based analysis for a real genome-wide association study
Gene hunting of the Genetic Analysis Workshop 16 rheumatoid arthritis data using rough set theory
We propose to use the rough set theory to identify genes affecting rheumatoid arthritis risk from the data collected by the North American Rheumatoid Arthritis Consortium. For each gene, we employ generalized dynamic reducts in the rough set theory to select a subset of single-nucleotide polymorphisms (SNPs) to represent the genetic information from this gene. We then group the study subjects into different clusters based on their genotype similarity at the selected markers. Statistical association between disease status and cluster membership is then studied to identify genes associated with rheumatoid arthritis. Based on our proposed approach, we are able to identify a number of statistically significant genes associated with rheumatoid arthritis. Aside from genes on chromosome 6, our identified genes include known disease-associated genes such as PTPN22 and TRAF1. In addition, our list contains other biologically plausible genes, such as ADAM15 and AGPAT2. Our findings suggest that ADAM15 and AGPAT2 may contribute to a genetic predisposition through abnormal angiogenesis and adipose tissue
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