Zan Armstrong
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View article: Discovery of complex oxides via automated experiments and data science
Discovery of complex oxides via automated experiments and data science Open
Significance Automation is accelerating the discovery of useful materials, yet testing even a small fraction of the billions of possible materials for a desired property is beyond the reach of workflows involving resource-intensive propert…
View article: Impacts of social distancing policies on mobility and COVID-19 case growth in the US
Impacts of social distancing policies on mobility and COVID-19 case growth in the US Open
Social distancing remains an important strategy to combat the COVID-19 pandemic in the United States. However, the impacts of specific state-level policies on mobility and subsequent COVID-19 case trajectories have not been completely quan…
View article: Protocol to generate DNA aptamer coated particles and utilization for affinity-based screening with particle display
Protocol to generate DNA aptamer coated particles and utilization for affinity-based screening with particle display Open
Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries…
View article: Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts
Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts Open
Drug discovery for diseases such as Parkinson’s disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painti…
View article: A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens
A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens Open
Machine learning is applied to high-throughput microscopy images of malaria parasites to define antimalarial drug mode of action.
View article: A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens
A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens Open
Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current …
View article: Activation Atlas
Activation Atlas Open
By using feature inversion to visualize millions of activations from an image classification network, we create an explorable activation atlas of features the network has learned and what concepts it typically represents.