PathFlowAI: A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation in Digital Pathology

The diagnosis of disease often requires analysis of a biopsy. Many diagnoses depend not only on the presence of certain features but on their location within the tissue. Recently, a number of deep learning diagnostic aids have been developed to classify digitized biopsy slides. Clinical workflows often involve processing of more than 500 slides per day. But, clinical use of deep learning diagnostic aids would require a preprocessing workflow that is cost-effective, flexible, scalable, rapid, interpretable, and transparent. Here, we present such a workflow, optimized using Dask and mixed precision training via APEX, capable of handling any patch-level or slide level classification and prediction problem. The workflow uses a flexible and fast preprocessing and deep learning analytics pipeline, incorporates model interpretation and has a highly storage-efficient audit trail. We demonstrate the utility of this package on the analysis of a prototypical anatomic pathology specimen, liver biopsies for evaluation of hepatitis from a prospective cohort. The preliminary data indicate that PathFlowAI may become a cost-effective and time-efficient tool for clinical use of Artificial Intelligence (AI) algorithms.

Integration of the Rosetta suite with the python software stack via reproducible packaging and core programming interfaces for distributed simulation

The Rosetta software suite for macromolecular modeling is a powerful computational toolbox for protein design, structure prediction, and protein structure analysis. The development of novel Rosetta-based scientific tools requires two orthogonal skill sets: deep domain-specific expertise in protein biochemistry and technical expertise in development, deployment, and analysis of molecular simulations. Furthermore, the computational demands of molecular simulation necessitate large scale cluster-based or distributed solutions for nearly all scientifically relevant tasks. To reduce the technical barriers to entry for new development, we integrated Rosetta with modern, widely adopted computational infrastructure. This allows simplified deployment in large-scale cluster and cloud computing environments, and effective reuse of common libraries for simulation execution and data analysis. To achieve this, we integrated Rosetta with the Conda package manager; this simplifies installation into existing computational environments and packaging as docker images for cloud deployment. Then, we developed programming interfaces to integrate Rosetta with the PyData stack for analysis and distributed computing, including the popular tools Jupyter, Pandas, and Dask. We demonstrate the utility of these components by generating a library of a thousand de novo disulfide-rich miniproteins in a hybrid simulation that included cluster-based design and interactive notebook-based analyses. Our new tools enable users, who would otherwise not have access to the necessary computational infrastructure, to perform state-of-the-art molecular simulation and design with Rosetta.