Srinivas Niranj Chandrasekaran

The paper presents a comprehensive genetic perturbation dataset using Cell Painting to analyze gene expression effects in human cells, revealing novel gene clusters and functional relationships.

The paper improves drug target prediction by combining gene and compound morphological profiles using a transformer model, showing better performance than direct matching methods.

The paper introduces a dataset of cell morphology images treated with matched chemical and genetic perturbations and evaluates methods for identifying perturbation similarities.

The paper updates the Cell Painting assay protocol to improve morphological profiling, finding that simplified steps and reduced stain concentrations maintain robust outputs.

Cell Painting images and profiles for 115,796 compounds in U2OS cells from 12 laboratories; 15.2% of analyzed compounds are phenotypically active, and cross-laboratory replicates cut reference-compound activity by 34%.

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