Jiawen Carmen Chen

The paper introduces GCaMP8 variants targeted to synaptic compartments and develops CaFire software to quantify Ca2+ signals, showing improved sensitivity and resolution compared to existing methods.

This study builds prediction models for RBC alloimmunization using statistical and machine learning methods on the REDS-III database, finding XGBoost provides the best performance with an AUC-ROC of 0.752.

The paper identifies genetic and phenotypic correlates of mosaic loss of chromosome Y in blood using whole genome sequencing data from the TOPMed program.

MAST-Decon improves spatial transcriptomics deconvolution by incorporating spatial neighborhood and histological image features to enhance accuracy and reliability.

Admix-kit provides tools for genetic analysis of admixed populations, enabling simulation, association testing, and polygenic scoring.

This paper introduces PRIME, a high-throughput prime editing screen that identifies functional DNA variants in the human genome with base-pair resolution.

SnapFISH identifies chromatin loops from multiplexed DNA FISH data with high sensitivity and accuracy in mouse embryonic stem cells.

DeepGWAS enhances GWAS signals for neuropsychiatric disorders using deep neural networks, improving locus detection in schizophrenia and validating results in other disorders.

POLARIS infers cell composition from histology images and identifies layer-specific differentially expressed genes in spatial transcriptomics data.

This paper improves multi-omics integration by enhancing orthogonality in canonical vectors and demonstrates transferability across cohorts for proteomics and methylomics data.

GAUDI improves polygenic risk prediction in admixed populations by modeling ancestry-specific and shared effects, outperforming existing methods in simulations and real data.

This study prioritizes functional elements of ~148,000 GWAS variants for blood cell traits using a web tool, showing prioritized variants are more likely to be experimentally validated.

HPRep quantifies reproducibility in HiChIP and PLAC-seq data by using a stratified and weighted correlation metric, outperforming existing Hi-C-based measures.

This paper estimates direct and indirect genetic effects using summary statistics from multi-generational GWAS, showing distinct patterns for traits like educational attainment and autism spectrum disorder.