Chad L. Myers

This paper develops an AI-enabled platform to screen FDA-approved drugs for geroprotective effects, identifying 31 compounds that increase heat resistance in C. elegans and show anti-senescent activity in human cells.

The paper expands TheCellMap.org to include human genetic interactions from CRISPR-based analysis in HAP1 cells, enabling visualization and exploration of the network.

This paper introduces SCA, a computational method for identifying rare LSC populations in AML using scRNA-seq, and finds a conserved self-renewal program associated with poor prognosis.

The paper develops a method to quantify genetic interactions from CRISPR-Cas9 screens and identifies systematic variations in screen data.

The paper constructs a genome-scale genetic interaction network from 4 million double mutants in HAP1 cells, revealing conserved genetic network principles across species and uncovering novel gene functions and cancer dependencies.

This study identifies functional relationships between TSC22D, WNK, and NRBP gene families in macromolecular crowding sensing and cell volume regulation across metazoans.

The paper identifies PRC2.1's role in opposing G1 progression by regulating CCND1 and CCND2, showing its importance in cell proliferation across multiple cancer types.

The paper presents a scalable CRISPR-Cas9 platform for chemical-genetic screens of DNA damage-inducing compounds, identifying enriched modes-of-action and revealing late ICL repair pathway responses.

The paper shows that N-terminal acetylation by NatC protects proteins from degradation, enhancing longevity and motility, and that UBR4-KCMF1 targets unacetylated N-terminal Met followed by hydrophobic residues.

This study evaluates dimensionality reduction methods to normalize CRISPR screen data and improve functional gene network extraction from the DepMap.

The paper identifies widespread transcriptional regulation of metabolic genes in C. elegans and introduces a tool to explore gene-pathway associations.

The paper decomposes double-peak transcriptome responses in Arabidopsis during effector-triggered immunity into two single-peak patterns, revealing conserved responses in distinct cell populations.

The paper identifies limitations in current reproducibility metrics for CRISPR screens and advocates for context-sensitive metrics to better assess screen quality.

The paper benchmarks structure descriptors and similarity coefficients using chemical-genetic interaction data to improve compound function prediction.

This study compares host module activation patterns and temporal dynamics across four influenza H1N1 viruses to identify conserved and differentially regulated immune responses.

The paper introduces FLEX, a pipeline for benchmarking CRISPR screen data, and identifies a dominant mitochondria-associated signal in genome-wide screens.

The paper shows trans-eQTLs in yeast can affect adjacent genes through horizontal mechanisms, not just vertical pathways, and this is linked to physical proximity and shared chromatin states.

This study maps genetic interactions in FASN-deficient cells to identify genes involved in lipid uptake and metabolic adaptation.

This paper introduces BridGE, a method to detect pathway-level genetic interactions from GWAS data, revealing significant interactions in multiple diseases.

The paper develops Camoco, a method integrating GWAS loci with co-expression networks to prioritize causal genes in maize, showing improved performance with tissue-specific expression data.

The paper develops CG-TARGET to predict compound biological processes by integrating chemical-genetic and genetic interaction networks, showing improved accuracy and FDR control.

The paper integrates GWAS and co-expression networks to identify candidate genes associated with nodule-related phenotypes in Medicago truncatula.

The paper introduces MOSAIC, a database and web resource for chemical-genetic interaction data, enabling mode-of-action prediction and discovery of compound effects in yeast.

This study develops a yeast-based chemical-genetic screening system to annotate compound functionality across biological processes and identifies compounds with dual modes of action.

The paper introduces COMPRESS-GI, an algorithm that efficiently selects informative genes for genetic interaction screens, enabling large-scale chemical genetic screens with high throughput.

The paper evaluates CRISPR/Cas9 knockout screens in human cell lines and designs an optimized gRNA library to improve gene knockout efficiency and accuracy.

The paper measures pleiotropy in yeast genes using genetic interaction networks, finding high-pleiotropy genes have broader functional influence and distinct characteristics compared to low-pleiotropy genes.