Ian C. P. Smith

This paper introduces PeML, a similarity metric learning method for perturbational datasets, improving the identification of perturbation mechanisms and biological relationships.

The paper assesses the inter-lab consistency of circular RNA expression in cancer cell lines and identifies factors affecting reproducibility.

The paper shows that consensus signatures from the L1000 Connectivity Map exhibit similarity bias, which can be mitigated by mean normalization.

The paper introduces a cloud-based platform called ORCESTRA for reproducible processing and sharing of multimodal biomedical data in a FAIR manner.

This study develops RadioGx, a computational platform to analyze radioresponse in radiogenomic databases, identifying molecular pathways and drug correlations with radiation response.

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.

The paper introduces the L1000 platform, a high-throughput gene expression profiling method, and reports 1.3 million perturbational profiles expanding the Connectivity Map for drug mechanism discovery and functional annotation.