Abstract We have developed an image-analysis and classification system for automatically scoring images from high-throughput protein crystallization trials. Image analysis for this system is performed by the Help Conquer Cancer (HCC) project on the World Community Grid. HCC calculates 12,375 distinct image features on microbatch-under-oil images from the Hauptman-Woodward Medical Research Institute's High-Throughput Screening Laboratory. Using HCC-computed image features and a massive training set of 165,351 hand-scored images, we have trained multiple Random Forest classifiers that accurately recognize multiple crystallization outcomes, including crystals, clear drops, precipitate, and others. The system successfully recognizes 80% of crystal-bearing images, 89% of precipitate images, and 98% of clear drops.
Likewise, the automated system by Cumba et al. (2010) REF detected 80% of crystal-bearing images, 89% of precipitate images, and 98% of clear drops accurately.
Protein crystallization analysis on the World Community Grid
Journal of Structural and Functional Genomics
Journal of Structural and Functional Genomics
Biology Medicine