Fei Zou

This study uses Collaborative Cross mice to identify host genetic factors influencing Chlamydia muridarum infection outcomes, revealing distinct genetic pathways for bacterial burden and pathology.

PETScan performs genome-wide association analysis of RNA-Seq and ATAC-Seq data using negative binomial models and score tests, achieving faster computation than Wald tests while identifying similar significant gene-peak pairs.

GPTAnno provides a hierarchical cell type annotation method using GPT models and ontology trees, improving accuracy across species and tissues by integrating clustering with ontology constraints.

This paper introduces SurvDNN, a deep learning framework for survival outcome modeling in complex diseases, which outperforms existing methods in biomarker identification and prediction.

MITScan performs genome-wide association analysis between microbiome and host transcriptome using zero-inflated negative binomial models, achieving computational efficiency with score tests and permutation methods.

This paper introduces a deep neural network-based two-part model for semi-continuous data and demonstrates its improved performance over existing methods.

The paper identifies a glutamatergic signaling network involving GRM5, GCPII, and GRM3 as a genetic mediator of daily sleep patterns through QTL mapping in mice.

This paper introduces HiFIT, a framework for high-dimensional biomarker identification that improves disease prediction and feature importance detection using machine learning.

The paper characterizes hepatic lentiviral vector transduction in mice, finding strain-specific variations and QTLs linked to transduction efficiency and kinetics, suggesting the need for personalized gene therapy protocols.

ARTdeConv improves cell type deconvolution by addressing limitations in existing methods, showing better performance in simulations and real data compared to other approaches.

TwinEQTL provides a fast and powerful method for eQTL and GWAS analysis in twin studies by splitting twin samples and combining results with minimal power loss.

This paper introduces JGNsc, a method for constructing joint gene networks from scRNA-seq data, and applies it to cancer studies to identify novel findings.

The paper develops PEPPRMINT, a statistical model to predict HLA-I peptide presentation, and applies it to identify immunogenic neoantigens in melanoma patients.

This paper introduces a deep learning semiparametric regression method to adjust complex confounding structures in observational studies, showing reduced bias and mean squared error in parameter estimates.

The paper develops TReCASE, a statistical method for eQTL mapping using ASE data, showing it improves power and efficiency compared to existing methods.

This paper introduces PermFIT, a permutation-based method to assess feature importance in black-box models, showing it improves prediction accuracy and identifies biomarkers in cancer and BMI data.

The paper introduces BASE, a Bayesian pipeline for analyzing allelic imbalance to identify cis regulatory variation, showing it validates ~75% of predicted differences between non-tester alleles.

This study identifies genetic factors influencing antipsychotic-induced adverse reactions in mice using RIX lines, finding strong strain and treatment interactions for behavioral phenotypes.

SCDC improves bulk RNA-seq deconvolution by integrating multiple scRNA-seq reference datasets, achieving better accuracy in cell-type decomposition.

This paper investigates the performance of polygenic risk scores (PRS) for complex traits, showing how prediction accuracy depends on sample size, SNP count, and true causal SNP count.