Ted E. Natoli

The paper uses a chemical genetic approach with induced pluripotent stem cells to screen for compounds affecting SMA neuronal gene expression, generating a queryable dataset of transcriptional profiles.

The paper compares morphology and gene expression data to determine their complementary roles in mapping cell states across drug perturbations.

This study evaluates deconvolution methods for gene expression data using an open innovation competition and finds that random forest regression outperforms other approaches in accuracy and reproducibility.

The paper demonstrates how open innovation competitions improve computational biology methods for antibody clustering, gene expression imputation, and CMap querying.

The paper develops an in-vitro gene expression screening method to predict chemical carcinogenicity and genotoxicity using HepG2 cells and machine learning models.

The paper creates a proteomic resource profiling 90 drugs in six cell lines, revealing cell-specific responses and enabling integration with transcriptional data to identify therapeutic opportunities.

The paper introduces the GCTx format and associated software packages for efficiently storing and analyzing large dense matrices of perturbation data and annotations.

The paper evaluates RNAi and CRISPR technologies by analyzing gene expression profiles in the Connectivity Map, finding that RNAi has strong off-target effects while CRISPR is more accurate.