Catarina D. Campbell

Dissecting context-dependent cancer vulnerabilities using Perturb-seq

The paper uses Perturb-seq to analyze gene knockouts in cancer cell lines, identifying context-specific gene dependencies and transcriptional responses.

Tabular foundation model predicts alternative lengthening of telomeres (ALT) and identifies SMARCAL1 as a target in ALT-driven cancers

The paper develops ALTitude, a machine learning model to predict ALT status from whole genome sequencing data, identifies SMARCAL1 as a therapeutic target in ALT-driven cancers.

Natural language processing of gene descriptions for overrepresentation analysis with GeneTEA

GeneTEA uses natural language processing to analyze gene descriptions and improve overrepresentation analysis by creating a de novo gene set database that better controls false discovery.

Controlling False Discovery in CRISPR Screens

This paper introduces Chronos, a module for estimating false discovery rates in CRISPR screens, showing it outperforms existing methods like MAGeCK in controlling false positives.

A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data

This study benchmarks seven computational methods for correcting CN and proximity biases in CRISPR-Cas9 screens, finding AC-Chronos and CRISPRcleanR perform best in different scenarios.