Paul Pavlidis

This paper shows that gene function prediction from bulk coexpression is limited by cell-type-level resolution rather than fine-grained biochemical function.

SCADR assesses PTEN missense variants' effects on signaling pathways through single-cell phospho-antibody analysis, revealing variant-specific dysfunction profiles.

The paper describes a software tool using LLMs to automate data curation for genomics, achieving performance near human curators with lower cost and higher speed.

The paper assesses challenges in reusing scRNA-seq data from GEO, finding that only ~40% provide usable processed data and <10% include cell-type annotations.

This study evaluates coexpression analysis methods in single-cell and bulk data, finding that xCell provides more replicable and biologically interpretable results than other modes.

The paper integrates microglia morphology and gene expression using spatial transcriptomics and proteogenomics to identify morphology-related genes in the human brain.

This study shows that loss of brain insulin production in female mice impairs learning and memory, while having no effect on males.

This paper evaluates using language models to improve gene-expression dataset discovery in GEO by comparing embeddings to GEO's search engine.

The paper develops a yeast growth assay to assess the functional impact of ALDH5A1 variants linked to autism spectrum disorder, showing they range from loss of function to normal activity.

This paper evaluates using GPT-4o to annotate mouse strains and cell lines in genomics data, achieving 77% and 59% accuracy, and identifies manual curation errors.

The paper uses long-read sequencing to catalog Cacna1e splice variants in rat thalamus and computationally identifies four functionally distinct isoforms.

The paper assesses nine MECP2 variants in ASD using yeast and Drosophila assays, finding four as loss of function and five as functional, highlighting non-methyl DNA-binding roles.

The paper identifies reproducible transcription regulator coexpression patterns using single-cell RNA-seq data across human and mouse, showing consistency with ChIP-seq results.

The paper develops a computational simulator to study how intracellular regulatory signals propagate from single cells to bulk tissue expression data, finding that such propagation is rare and conditions are highly specific.

The paper develops an open-source pipeline to characterize microglia morphology at single-cell resolution using ImageJ and R, identifying distinct morphological states across brain regions and conditions.

The paper integrates ChIP-seq and TR perturbation data to rank human and mouse TR-target interactions and identify orthologous relationships.

The paper identifies small molecules that enhance stem cell differentiation into definitive endoderm, reducing costs by 90% through a drug repurposing approach.

ModelMatcher connects researchers, clinicians, and stakeholders in rare disease research to accelerate translational studies and collaborations.

This study curates experimentally verified transcriptional regulatory interactions from literature to build a high-confidence resource for gene regulatory network development.

This study evaluates the reproducibility of the Connectivity Map (CMap) for drug repositioning and finds limited reliability between CMap versions and low differential expression consistency.

The paper curates over 10,000 transcriptomic datasets to improve data reuse by standardizing metadata, processing, and annotations.

The paper analyzes 58 SYNGAP1 variants using multiple assays to assess their impact on GTPase signaling, localization, and protein stability, revealing functional differences and pathogenicity classifications.

This study evaluates machine learning methods for prioritizing autism spectrum disorder risk genes and finds they underperform compared to traditional genetic association approaches.

The paper examines how cellular composition affects coexpression analysis in human brain RNA data, finding that much of the observed coexpression is due to cell-type composition rather than regulatory relationships.

The paper evaluates 106 PTEN variants using 18 assays across 5 models to identify molecular mechanisms of dysfunction, finding that stability and domain-specific assays are critical for accurate impact assessment.

The paper uses single-nucleus RNA sequencing to identify gene expression changes in specific cell types associated with major depressive disorder.

This study systematically analyzes ASD-associated genes in C. elegans, identifying phenotypic relationships and reversible impairments in habituation learning.

VariCarta aggregates and harmonizes genomic variants from ASD sequencing studies to prevent double-counting and improve association evidence.

The paper identifies gene expression correlations with electrophysiological and morphological traits across neuron types, distinguishing class-driven from within-class relationships.

The paper evaluates the functional significance of splice isoforms in human and mouse genes, finding limited experimental evidence for functionally distinct splice isoforms.