Clare Pacini

The paper introduces a deep learning model to augment cancer cell line multi-omic data, increasing the number of profiles and uncovering drug resistance mechanisms.

The paper provides a dataset and R package to benchmark CRISPR-Cas9 screening pipelines using HT-29 cells and a commercial guide RNA library.

CoRe identifies core-fitness genes from CRISPR-Cas9 screens, outperforming existing tools and providing more reliable results.

The paper integrates two large CRISPR-Cas9 screens to improve the detection of gene dependencies and cancer vulnerabilities by addressing biases and batch effects.

The paper designs a smaller genome-wide CRISPR-Cas9 library with reduced size while maintaining sensitivity and specificity, enhancing its applicability in complex models.

This study shows that the ratio of periportal mesenchyme to ductal epithelial cells regulates ductal cell proliferation during liver regeneration through Notch signaling.

This study integrates drug sensitivity data with CRISPR screens to identify drug mechanisms and biomarkers for cancer therapy.

CELLector selects relevant cancer cell lines based on patient genomic data to improve in vitro model relevance and bridge cell line screens to primary tumor sub-cohorts.