Publications by authors named "Jeongyoun Ahn"

9 Publications

  • Page 1 of 1

Feature-weighted Ordinal Classification for Predicting Drug Response in Multiple Myeloma.

Bioinformatics 2021 May 11. Epub 2021 May 11.

Department of Statistics, University of Georgia, Athens, GA 30602, USA.

Motivation: Ordinal classification problems arise in a variety of real-world applications, in which samples need to be classified into categories with a natural ordering. An example of classifying high-dimensional ordinal data is to use gene expressions to predict the ordinal drug response, which has been increasingly studied in pharmacogenetics. Classical ordinal classification methods are typically not able to tackle high-dimensional data and standard high-dimensional classification methods discard the ordering information among the classes. Existing work of high-dimensional ordinal classification approaches usually assume a linear ordinality among the classes. We argue that manually-labeled ordinal classes may not be linearly arranged in the data space, especially in high-dimensional complex problems.

Results: We propose a new approach that can project high-dimensional data into a lower discriminating subspace, where the innate ordinal structure of the classes is uncovered. The proposed method weights the features based on their rank correlations with the class labels and incorporates the weights into the framework of linear discriminant analysis. We apply the method to predict the response to two types of drugs for patients with Multiple Myeloma, respectively. A comparative analysis with both ordinal and nominal existing methods demonstrates that the proposed method can achieve a competitive predictive performance while honoring the intrinsic ordinal structure of the classes. We provide interpretations on the genes that are selected by the proposed approach to understand their drug-specific response mechanisms.

Availability And Implementation: The data underlying this article are available in the Gene Expression Omnibus Database at https://www.ncbi.nlm.nih.gov/geo/ and can be accessed with accession number GSE9782 and GSE68871. The source code for FWOC can be accessed at https://github.com/pisuduo/Feature-Weighted-Ordinal-Classification-FWOC.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btab320DOI Listing
May 2021

An integrative multivariate approach for predicting functional recovery using magnetic resonance imaging parameters in a translational pig ischemic stroke model.

Neural Regen Res 2021 May;16(5):842-850

Regenerative Bioscience Center; Neuroscience, Biomedical and Health Sciences Institute; Department of Animal and Dairy Science, College of Agricultural and Environmental Sciences, University of Georgia, Athens, GA, USA.

Magnetic resonance imaging (MRI) is a clinically relevant, real-time imaging modality that is frequently utilized to assess stroke type and severity. However, specific MRI biomarkers that can be used to predict long-term functional recovery are still a critical need. Consequently, the present study sought to examine the prognostic value of commonly utilized MRI parameters to predict functional outcomes in a porcine model of ischemic stroke. Stroke was induced via permanent middle cerebral artery occlusion. At 24 hours post-stroke, MRI analysis revealed focal ischemic lesions, decreased diffusivity, hemispheric swelling, and white matter degradation. Functional deficits including behavioral abnormalities in open field and novel object exploration as well as spatiotemporal gait impairments were observed at 4 weeks post-stroke. Gaussian graphical models identified specific MRI outputs and functional recovery variables, including white matter integrity and gait performance, that exhibited strong conditional dependencies. Canonical correlation analysis revealed a prognostic relationship between lesion volume and white matter integrity and novel object exploration and gait performance. Consequently, these analyses may also have the potential of predicting patient recovery at chronic time points as pigs and humans share many anatomical similarities (e.g., white matter composition) that have proven to be critical in ischemic stroke pathophysiology. The study was approved by the University of Georgia (UGA) Institutional Animal Care and Use Committee (IACUC; Protocol Number: A2014-07-021-Y3-A11 and 2018-01-029-Y1-A5) on November 22, 2017.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.4103/1673-5374.297079DOI Listing
May 2021

Perinatal Docosahexaenoic Acid Supplementation Improves Cognition and Alters Brain Functional Organization in Piglets.

Nutrients 2020 Jul 15;12(7). Epub 2020 Jul 15.

Department of Foods and Nutrition, College of Family and Consumer Sciences, University of Georgia, Athens, GA 30602, USA.

Epidemiologic studies associate maternal docosahexaenoic acid (DHA)/DHA-containing seafood intake with enhanced cognitive development; although, it should be noted that interventional trials show inconsistent findings. We examined perinatal DHA supplementation on cognitive performance, brain anatomical and functional organization, and the brain monoamine neurotransmitter status of offspring using a piglet model. Sows were fed a control (CON) or a diet containing DHA (DHA) from late gestation throughout lactation. Piglets underwent an open field test (OFT), an object recognition test (ORT), and magnetic resonance imaging (MRI) to acquire anatomical, diffusion tensor imaging (DTI), and resting-state functional MRI (rs-fMRI) at weaning. Piglets from DHA-fed sows spent 95% more time sniffing the walls than CON in OFT and exhibited an elevated interest in the novel object in ORT, while CON piglets demonstrated no preference. Maternal DHA supplementation increased fiber length and tended to increase fractional anisotropy in the hippocampus of offspring than CON. DHA piglets exhibited increased functional connectivity in the cerebellar, visual, and default mode network and decreased activity in executive control and sensorimotor network compared to CON. The brain monoamine neurotransmitter levels did not differ in healthy offspring. Perinatal DHA supplementation may increase exploratory behaviors, improve recognition memory, enhance fiber tract integrity, and alter brain functional organization in offspring at weaning.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/nu12072090DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400913PMC
July 2020

Sparse generalized eigenvalue problem with application to canonical correlation analysis for integrative analysis of methylation and gene expression data.

Biometrics 2018 12 11;74(4):1362-1371. Epub 2018 May 11.

Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania, U.S.A.

We present a method for individual and integrative analysis of high dimension, low sample size data that capitalizes on the recurring theme in multivariate analysis of projecting higher dimensional data onto a few meaningful directions that are solutions to a generalized eigenvalue problem. We propose a general framework, called SELP (Sparse Estimation with Linear Programming), with which one can obtain a sparse estimate for a solution vector of a generalized eigenvalue problem. We demonstrate the utility of SELP on canonical correlation analysis for an integrative analysis of methylation and gene expression profiles from a breast cancer study, and we identify some genes known to be associated with breast carcinogenesis, which indicates that the proposed method is capable of generating biologically meaningful insights. Simulation studies suggest that the proposed method performs competitive in comparison with some existing methods in identifying true signals in various underlying covariance structures.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/biom.12886DOI Listing
December 2018

Clustering multivariate functional data with phase variation.

Biometrics 2017 03 24;73(1):324-333. Epub 2016 May 24.

Department of Statistics, University of Georgia, Athens, Georgia 30602-1952, U.S.A.

When functional data come as multiple curves per subject, characterizing the source of variations is not a trivial problem. The complexity of the problem goes deeper when there is phase variation in addition to amplitude variation. We consider clustering problem with multivariate functional data that have phase variations among the functional variables. We propose a conditional subject-specific warping framework in order to extract relevant features for clustering. Using multivariate growth curves of various parts of the body as a motivating example, we demonstrate the effectiveness of the proposed approach. The found clusters have individuals who show different relative growth patterns among different parts of the body.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/biom.12546DOI Listing
March 2017

Covariance adjustment for batch effect in gene expression data.

Stat Med 2014 Jul 28;33(15):2681-95. Epub 2014 Mar 28.

Division of Public Health Sciences, Washington University in St. Louis, St. Louis, MO 63110, U.S.A.

Batch bias has been found in many microarray gene expression studies that involve multiple batches of samples. A serious batch effect can alter not only the distribution of individual genes but also the inter-gene relationships. Even though some efforts have been made to remove such bias, there has been relatively less development on a multivariate approach, mainly because of the analytical difficulty due to the high-dimensional nature of gene expression data. We propose a multivariate batch adjustment method that effectively eliminates inter-gene batch effects. The proposed method utilizes high-dimensional sparse covariance estimation based on a factor model and a hard thresholding. Another important aspect of the proposed method is that if it is known that one of the batches is produced in a superior condition, the other batches can be adjusted so that they resemble the target batch. We study high-dimensional asymptotic properties of the proposed estimator and compare the performance of the proposed method with some popular existing methods with simulated data and gene expression data sets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/sim.6157DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4065794PMC
July 2014

A multiscale analysis of the temporal characteristics of resting-state fMRI data.

J Neurosci Methods 2010 Nov 9;193(2):334-42. Epub 2010 Sep 9.

Department of Statistics, University of Georgia, Athens, GA 30602, USA.

In this paper, we conduct an investigation of the null hypothesis distribution for functional magnetic resonance imaging (fMRI) time series using multiscale analysis tools, SiZer (significance of zero crossings of the derivative) and wavelets. Most current approaches to the analysis of fMRI data assume simple models for temporal (short term or long term) dependence structure. Such simplifications are to some extent necessary due to the complex, high-dimensional nature of the data, but to date there have been few systematic studies of the dependence structures under a range of possible null hypotheses, using data sets gathered specifically for that purpose. We aim to address some of these issues by analyzing the detrended data with a long enough time horizon to study possible long-range temporal dependence. Our multiscale approach shows that even for resting-state data, data, i.e. "null" or ambient thought, some voxel time series cannot be modeled by white noise and need long-range dependent type error structure. This finding suggests the use of different time series models in different parts of the brain in fMRI studies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jneumeth.2010.08.021DOI Listing
November 2010

Gene selection using support vector machines with non-convex penalty.

Bioinformatics 2006 Jan 25;22(1):88-95. Epub 2005 Oct 25.

Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.

Motivation: With the development of DNA microarray technology, scientists can now measure the expression levels of thousands of genes simultaneously in one single experiment. One current difficulty in interpreting microarray data comes from their innate nature of 'high-dimensional low sample size'. Therefore, robust and accurate gene selection methods are required to identify differentially expressed group of genes across different samples, e.g. between cancerous and normal cells. Successful gene selection will help to classify different cancer types, lead to a better understanding of genetic signatures in cancers and improve treatment strategies. Although gene selection and cancer classification are two closely related problems, most existing approaches handle them separately by selecting genes prior to classification. We provide a unified procedure for simultaneous gene selection and cancer classification, achieving high accuracy in both aspects.

Results: In this paper we develop a novel type of regularization in support vector machines (SVMs) to identify important genes for cancer classification. A special nonconvex penalty, called the smoothly clipped absolute deviation penalty, is imposed on the hinge loss function in the SVM. By systematically thresholding small estimates to zeros, the new procedure eliminates redundant genes automatically and yields a compact and accurate classifier. A successive quadratic algorithm is proposed to convert the non-differentiable and non-convex optimization problem into easily solved linear equation systems. The method is applied to two real datasets and has produced very promising results.

Availability: MATLAB codes are available upon request from the authors.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/bti736DOI Listing
January 2006

Blood transfusion is an independent predictor of increased mortality in nonoperatively managed blunt hepatic and splenic injuries.

J Trauma 2005 Mar;58(3):437-44; discussion 444-5

Section of Trauma, Burns, and Critical Care, Department of Surgery, University of North Carolina, Chapel Hill, North Carolina, USA.

Background: Management strategies for blunt solid viscus injuries often include blood transfusion. However, transfusion has previously been identified as an independent predictor of mortality in unselected trauma admissions. We hypothesized that transfusion would adversely affect mortality and outcome in patients presenting with blunt hepatic and splenic injuries after controlling for injury severity and degree of shock.

Methods: We retrospectively reviewed records from all adults with blunt hepatic and/or splenic injuries admitted to a Level I trauma center over a 4-year period. Demographics, physiologic variables, injury severity, and amount of blood transfused were analyzed. Univariate and multivariate analysis with logistic and linear regression were used to identify predictors of mortality and outcome.

Results: One hundred forty-three (45%) of 316 patients presenting with blunt hepatic and/or splenic injuries received blood transfusion within the first 24 hours. Two hundred thirty patients (72.8%) were selected for nonoperative management, of whom 75 (33%) required transfusion in the first 24 hours. Transfusion was an independent predictor of mortality in all patients (odds ratio [OR], 4.75; 95% confidence interval [CI], 1.37-16.4; p = 0.014) and in those managed nonoperatively (OR, 8.45; 95% CI, 1.95-36.53; p = 0.0043) after controlling for indices of shock and injury severity. The risk of death increased with each unit of packed red blood cells transfused (OR per unit, 1.16; 95% CI, 1.10-1.24; p < 0.0001). Blood transfusion was also an independent predictor of increased hospital length of stay (coefficient, 5.45; 95% CI, 1.64-9.25; p = 0.005).

Conclusion: Blood transfusion is a strong independent predictor of mortality and hospital length of stay in patients with blunt liver and spleen injuries after controlling for indices of shock and injury severity. Transfusion-associated mortality risk was highest in the subset of patients managed nonoperatively. Prospective examination of transfusion practices in treatment algorithms of blunt hepatic and splenic injuries is warranted.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/01.ta.0000153935.18997.14DOI Listing
March 2005