Cryo-electron microscopy is a popular method for the determination of protein structures; however, identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require ad hoc postprocessing, especially for unusually shaped particles. To address these shortcomings, we develop Topaz, an efficient and accurate particle-picking pipeline using neural networks trained with a general-purpose positive-unlabeled learning method. This framework enables particle detection models to be trained with few sparsely labeled particles and no labeled negatives. Topaz retrieves many more real particles than conventional picking methods while maintaining low false-positive rates, is capable of picking challenging unusually shaped proteins (for example, small, non-globular and asymmetric particles), produces more representative particle sets and does not require post hoc curation. We demonstrate the performance of Topaz on two difficult datasets and three conventional datasets. Topaz is modular, standalone, free and open source (http://topaz.csail.mit.edu).
#DigitalSurgery: The Robot Will Assist the Surgeon Now. @ShafiAhmed5 on the convergence of #AR, #VR #AI & #Robotics on augmenting the clinician of the future. https://t.co/hpWqv5D2Yw Join us next week for #xMed 2019. https://t.co/La9S00SM8Z #MedEd #hcldr #surgery #digitalHealth— Exponential Medicine (@ExponentialMed) October 30, 2019