Ali Shojaie

This paper introduces a framework to improve survival prediction in spatial proteomics by incorporating spatially adjusted protein summaries into Cox models, showing better performance in simulations and applications to breast cancer data.

The paper introduces a data model and dataset for rare disease research, enabling interoperable multi-omic data integration across clinical sites.

The paper introduces a pipeline to create 3D spatial atlases for brain tissue samples, enabling standardized referencing and integration across biorepositories and neuroimaging data.

CausalGRN infers causal gene regulatory networks from single-cell CRISPR screens and predicts responses to unseen perturbations with improved accuracy.

The paper introduces TWiST, a method for transcriptome-wide association studies at cell state resolution using single-cell eQTL data, showing improved power in detecting dynamic gene-disease associations.

This study uses DCC-GARCH modeling on rs-fMRI data to analyze dynamic functional connectivity in Alzheimer's disease, showing improved sensitivity to CSF Aβ status compared to current methods.

The paper introduces SpaceDecorr, a method to reduce false-positive correlations in spatial transcriptomics data by adjusting for spatial dependencies and technical artifacts.

The paper addresses challenges in differential expression analysis of spatial transcriptomics data by mitigating segmentation errors and accounting for spatial correlation, reducing false discoveries.

The paper estimates mCA fitness in the NHLBI TOPMed cohort and identifies genetic variants associated with clonal expansion rates.

The paper identifies impactful rare variants by integrating multi-omic data from MESA, showing that combining expression and protein data increases the detection of rare stop and frameshift variants.

The paper identifies transcriptomically distinct microglia populations in Alzheimer's disease and aged human brains using single-nucleus RNA-seq and trajectory inference.

This study shows that pre-existing cortical network structure, rather than stimulation protocol alone, primarily determines functional reorganization induced by optogenetic stimulation in non-human primates.

The paper introduces REHE and reREHE methods for fast and robust variance components estimation in linear mixed models, providing non-negative estimates comparable to REML.

This study shows genetic associations of CRP variants with BMI differ between individuals with and without cardiometabolic diseases, suggesting BMI modifies these genetic effects.

This study compares nine pathway enrichment methods, finding that methods using both differential expression and pathway topology perform better on metabolomics data.

The paper introduces the GMD-biplot, a novel visualization method for microbiome data that improves clustering and variable-sample relationships compared to traditional PCoA plots.