Paul Pavlidis

The paper introduces GOTrack, a tool to monitor changes in the Gene Ontology and assess the stability of enrichment results over time for gene lists.

This study assesses patch-seq, showing technical artifacts in single-cell transcriptomes and proposing a marker gene-based approach to improve gene expression-electrophysiological feature alignment.

The paper compiles a database of mouse brain cell type-specific gene expression profiles, identifies novel marker genes, and demonstrates their use in analyzing bulk tissue data.

The paper identifies 420 genes whose expression correlates with electrophysiological parameters in neurons, suggesting general principles linking gene expression to functional diversity.

EGAD provides fast functional analysis of gene networks by predicting novel gene group members and assessing network performance using guilt-by-association methods.

This paper evaluates the robustness of gene set analysis results and introduces a method to identify non-specific and non-robust findings, while providing software for bias correction.

The paper identifies widespread sample mislabeling in transcriptomics studies by comparing sex annotations with gene expression data, finding 46% of datasets have mislabeled samples.

The paper imports human and mouse gene and protein data into Wikidata to improve data integration and dissemination, enabling updates to Wikipedia Gene Wiki infoboxes and multilingual access.