54 citations · 208 across the 29 of their papers we have counts for
30 papers
A general kernel boosting framework integrating pathways for predictive modeling based on genomic data
Li Zeng, Zhaolong Yu, Yiliang Zhang +1
Predictive modeling based on genomic data has gained popularity in biomedical research and clinical practice by allowing researchers and clinicians to identify biomarkers and tailo…
A set of efficient methods to generate high-dimensional binary data with specified correlation structures
Wei Jiang, Shuang Song, Lin Hou +1
High dimensional correlated binary data arise in many areas, such as observed genetic variations in biomedical research. Data simulation can help researchers evaluate efficiency an…
A Manifold Proximal Linear Method for Sparse Spectral Clustering with Application to Single-Cell RNA Sequencing Data Analysis
Zhongruo Wang, Bingyuan Liu, Shixiang Chen +3
Spectral clustering is one of the fundamental unsupervised learning methods widely used in data analysis. Sparse spectral clustering (SSC) imposes sparsity to the spectral clusteri…
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
Xiaochen Wang, Arash Pakbin, Bobak J. Mortazavi +2
The proliferation of medical monitoring devices makes it possible to track health vitals at high frequency, enabling the development of dynamic health risk scores that change with…
Inference of Dynamic Graph Changes for Functional Connectome
Dingjue Ji, Junwei Lu, Yiliang Zhang +2
Dynamic functional connectivity is an effective measure for the brain's responses to continuous stimuli. We propose an inferential method to detect the dynamic changes of brain net…
Phylogeny-based tumor subclone identification using a Bayesian feature allocation model
Li Zeng, Joshua L. Warren, Hongyu Zhao
Tumor cells acquire different genetic alterations during the course of evolution in cancer patients. As a result of competition and selection, only a few subgroups of cells with di…
A pathway-based kernel boosting method for sample classification using genomic data
Li Zeng, Zhaolong Yu, Hongyu Zhao
The analysis of cancer genomic data has long suffered "the curse of dimensionality". Sample sizes for most cancer genomic studies are a few hundreds at most while there are tens of…
Prediction analysis for microbiome sequencing data
Tao Wang, Can Yang, Hongyu Zhao
One primary goal of human microbiome studies is to predict host traits based on human microbiota. However, microbial community sequencing data present significant challenges to the…
On the Convergence of the EM Algorithm: A Data-Adaptive Analysis
Chong Wu, Can Yang, Hongyu Zhao +1
The Expectation-Maximization (EM) algorithm is an iterative method to maximize the log-likelihood function for parameter estimation. Previous works on the convergence analysis of t…
Joint Models for Time-to-Event Data and Longitudinal Biomarkers of High Dimension
Molei Liu, Jiehuan Sun, Jose D. Herazo-Maya +2
Joint models for longitudinal biomarkers and time-to-event data are widely used in longitudinal studies. Many joint modeling approaches have been proposed to deal with different ty…
A Bayesian Semiparametric Factor Analysis Model for Subtype Identification
Jiehuan Sun, Joshua L. Warren, Hongyu Zhao
Disease subtype identification (clustering) is an important problem in biomedical research. Gene expression profiles are commonly utilized to infer disease subtypes, which often le…
Distance-Correlation based Gene Set Analysis in Longitudinal Studies
Jiehuan Sun, Jose D. Herazo-Maya, Xiu Huang +2
Longitudinal gene expression profiles of patients are collected in some clinical studies to monitor disease progression and understand disease etiology. The identification of gene…
A Dirichlet Process Mixture Model for Clustering Longitudinal Gene Expression Data
Jiehuan Sun, Jose D. Herazo-Maya, Naftali Kaminski +2
Subgroup identification (clustering) is an important problem in biomedical research. Gene expression profiles are commonly utilized to define subgroups. Longitudinal gene expressio…
On Joint Estimation of Gaussian Graphical Models for Spatial and Temporal Data
Zhixiang Lin, Tao Wang, Can Yang +1
In this paper, we first propose a Bayesian neighborhood selection method to estimate Gaussian Graphical Models (GGMs). We show the graph selection consistency of this method in the…
A Markov random field-based approach to characterizing human brain development using spatial-temporal transcriptome data
Zhixiang Lin, Stephan J. Sanders, Mingfeng Li +3
Human neurodevelopment is a highly regulated biological process. In this article, we study the dynamic changes of neurodevelopment through the analysis of human brain microarray da…
NBLDA: Negative Binomial Linear Discriminant Analysis for RNA-Seq Data
Kai Dong, Hongyu Zhao, Xiang Wan +1
RNA-sequencing (RNA-Seq) has become a powerful technology to characterize gene expression profiles because it is more accurate and comprehensive than microarrays. Although statisti…
Detection boundary and Higher Criticism approach for rare and weak genetic effects
Zheyang Wu, Yiming Sun, Shiquan He +3
Genome-wide association studies (GWAS) have identified many genetic factors underlying complex human traits. However, these factors have explained only a small fraction of these tr…
High-dimensional genome-wide association study and misspecified mixed model analysis
Jiming Jiang, Cong Li, Debashis Paul +2
We study behavior of the restricted maximum likelihood (REML) estimator under a misspecified linear mixed model (LMM) that has received much attention in recent gnome-wide associat…
GPA: A statistical approach to prioritizing GWAS results by integrating pleiotropy information and annotation data
Dongjun Chung, Can Yang, Cong Li +2
Genome-wide association studies (GWAS) suggests that a complex disease is typically affected by many genetic variants with small or moderate effects. Identification of these risk v…
Low-Rank Modeling and Its Applications in Image Analysis
Xiaowei Zhou, Can Yang, Hongyu Zhao +1
Low-rank modeling generally refers to a class of methods that solve problems by representing variables of interest as low-rank matrices. It has achieved great success in various fi…
A Spatial Simulation Approach to Account for Protein Structure When Identifying Non-Random Somatic Mutations
Gregory Ryslik, Yuwei Cheng, Kei-Hoi Cheung +4
Background: Current research suggests that a small set of "driver" mutations are responsible for tumorigenesis while a larger body of "passenger" mutations occurs in the tumor but…
Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model
Mengjie Chen, Zhao Ren, Hongyu Zhao +1
A tuning-free procedure is proposed to estimate the covariate-adjusted Gaussian graphical model. For each finite subgraph, this estimator is asymptotically normal and efficient. As…
Change Point Analysis of Histone Modifications Reveals Epigenetic Blocks Linking to Physical Domains
Mengjie Chen, Haifan Lin, Hongyu Zhao
Histone modification is a vital epigenetic mechanism for transcriptional control in eukaryotes. High-throughput techniques have enabled whole-genome analysis of histone modificatio…
Posterior Contraction Rates of the Phylogenetic Indian Buffet Processes
Mengjie Chen, Chao Gao, Hongyu Zhao
By expressing prior distributions as general stochastic processes, nonparametric Bayesian methods provide a flexible way to incorporate prior knowledge and constrain the latent str…