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SUMMARY The pancreatic islets of Langerhans regulate glucose homeostasis. The loss of insulin-producing β cells within islets results in diabetes, and islet transplantation from cadaveric donors can cure the disease. In vitro production of whole islets, not just β cells, will benefit from a better understanding of endocrine differentiation and islet morphogenesis. We used single-cell mRNA sequencing to obtain a detailed description of pancreatic islet development. Contrary to the prevailing dogma, we find islet morphology and endocrine differentiation to be directly related. As endocrine progenitors differentiate, they migrate in cohesion and form bud-like islet precursors, or “peninsulas” (literally “almost islands”). α cells, the first to develop, constitute the peninsular outer layer, and β cells form later, beneath them. This spatiotemporal collinearity leads to the typical core-mantle architecture of the mature, spherical islet. Finally, we induce peninsula-like structures in differentiating human embryonic stem cells, laying the ground for the generation of entire islets in vitro.

We introduce AutoGluon-Tabular, an opensource 2 AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file. Unlike existing AutoML frameworks that primarily focus on model/hyperparameter selection, AutoGluon-Tabular succeeds by ensembling multiple models and stacking them in multiple layers. Experiments reveal that our multi-layer combination of many models offers better use of allocated training time than seeking out the best.A second contribution is an extensive evaluation of public and commercial AutoML platforms including TPOT, H2O, AutoWEKA, auto-sklearn, AutoGluon, and Google AutoML Tables. Tests on a suite of 50 classification and regression tasks from Kaggle and the OpenML AutoML Benchmark reveal that AutoGluon is faster, more robust, and much more accurate. We find that AutoGluon often even outperforms the best-in-hindsight combination of all of its competitors. In two popular Kaggle competitions, AutoGluon beat 99% of the participating data scientists after merely 4h of training on the raw data.

Motivation The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties. Results Here, we present Ens-Grad, a machine learning method that can design complementarity determining regions of human Immunoglobulin G antibodies with target affinities that are superior to candidates derived from phage display panning experiments. We also demonstrate that machine learning can improve target specificity by the modular composition of models from different experimental campaigns, enabling a new integrative approach to improving target specificity. Our results suggest a new path for the discovery of therapeutic molecules by demonstrating that predictive and differentiable models of antibody binding can be learned from high-throughput experimental data without the need for target structural data. Availability and implementation Sequencing data of the phage panning experiment are deposited at NIH’s Sequence Read Archive (SRA) under the accession number SRP158510. We make our code available at https://github.com/gifford-lab/antibody-2019. Supplementary information Supplementary data are available at Bioinformatics online.