It’s also noteworthy that the model with all three data modalities (CS+GE+MO) actually performed worse! This was only 1 more assay than cell morphology alone was able to accurately predict. It turned out that the best predictor was the model using chemical structures and Cell Paint morphology profiles (CS+MO) which could accurately predict 27 assays.
For this post, I’ll be highlighting the updated version of their exciting preprint entitled " Predicting compound activity from phenotypic profiles and chemical structures ” which was led by Juan C.
#Cellprofiler cell painting names software#
This group is best known for the development of the Cell Painting assay and the CellProfiler image analysis software (now maintained by the Cimini Lab ), which have both been hugely influential. One of the pioneering groups in this space has been the Carpenter-Singh Lab 3 at the Broad Institute. While this world didn’t quite come into fruition, and we aren’t (yet) all cloud biologists running our assays via HTTP requests, computational modeling has become a foundational guiding force for experimental design for some fields, including drug discovery. 1 I also became deeply inspired by a startup called Transcriptic (now Strateos ) which was offering an API to do experiments over the Internet in a “cloud robotic lab.”īased on these two data points, I wildly extrapolated early on that over the course of my career all biologists would be computationally modeling cells and tissues, and sending experimental designs off to cloud labs to validate them. Around that time, I read a paper from Bob Murphy’s lab at Carnegie Mellon that described a machine learning framework for predicting the results of a large experiment screen while only doing 29% of the experiments. I became really interested in this general problem, and wondered about ways that it might be made more efficient. I realized that drug discovery is an incredibly difficult search problem, requiring intensive laboratory automation and robotics to screen an enormous number of compounds in a wide variety of experimental assays.
In one of the projects I worked on, we ran some of our assays at the Quellos High-Throughput Screening Core, which completely blew my mind. I learned this early on in my research career while working at the UW Cancer Vaccine Institute. Discovering new drugs is an expensive and challenging process.
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