7 citations · 11 across the 11 of their papers we have counts for
13 papers
Annotation-Informed Block-Sparse Bayesian Modeling for cis-Expression Prediction
Lei Huang, Hui Shen, Kuan-Jui Su +9
Genotype-based cis-expression prediction depends on accurately modeling local regulatory architecture. We present block-sparse Bayesian sparse linear mixed model (bsBSLMM), an exte…
DXA-Derived Skeletal Phenotypes and Hip Fracture Risk: A Backdoor-Adjusted Causal Analysis
Zixin Shi, Chen Zhao, Meiling Zhou +8
Purpose: To compare dual-energy X-ray absorptiometry (DXA)-derived hip skeletal phenotypes in relation to hip fracture risk using prespecified confounder adjustment and to assess w…
GenoBERT: A Language Model for Accurate Genotype Imputation
Lei Huang, Chuan Qiu, Kuan-Jui Su +13
Genotype imputation enables dense variant coverage for genome-wide association and risk-prediction studies, yet conventional reference-panel methods remain limited by ancestry bias…
Improving Generalizability of Hip Fracture Risk Prediction via Domain Adaptation Across Multiple Cohorts
Shuo Sun, Meiling Zhou, Chen Zhao +8
Clinical risk prediction models often fail to be generalized across cohorts because underlying data distributions differ by clinical site, region, demographics, and measurement pro…
PF-DAformer: Proximal Femur Segmentation via Domain Adaptive Transformer for Dual-Center QCT
Rochak Dhakal, Chen Zhao, Zixin Shi +6
Quantitative computed tomography (QCT) plays a crucial role in assessing bone strength and fracture risk by enabling volumetric analysis of bone density distribution in the proxima…
An Advanced Two-Stage Model with High Sensitivity and Generalizability for Prediction of Hip Fracture Risk Using Multiple Datasets
Shuo Sun, Meiling Zhou, Chen Zhao +8
Hip fractures are a major cause of disability, mortality, and healthcare burden in older adults, underscoring the need for early risk assessment. However, commonly used tools such…
ICGM-FRAX: Iterative Cross Graph Matching for Hip Fracture Risk Assessment using Dual-energy X-ray Absorptiometry Images
Chen Zhao, Anjum Shaik, Joyce H. Keyak +7
Hip fractures represent a major health concern, particularly among the elderly, often leading decreased mobility and increased mortality. Early and accurate detection of at risk in…
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis
Yipu Zhang, Likai Wang, Kuan-Jui Su +7
Resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity networks (FCNs) have become critical for understanding neurological disorders.…
A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture
Anjum Shaik, Kristoffer Larsen, Nancy E. Lane +8
Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in…
Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics
Chen Zhao, Kuan-Jui Su, Chong Wu +13
Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing…
Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort
Anton Orlichenko, Kuan-Jui Su, Qing Tian +3
Purpose: Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been ob…
Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results
Anton Orlichenko, Gang Qu, Kuan-Jui Su +4
Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connec…
Multi-view information fusion using multi-view variational autoencoders to predict proximal femoral strength
Chen Zhao, Joyce H Keyak, Xuewei Cao +10
The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. Method: We developed new models using multi…