How Comedian Tim Heidecker Turned Alex Jones’ Conspiracy Empire on Its Head

For two years, after losing a landmark defamation case filed by the Sandy Hook families and declaring bankruptcy, Jones has ...
C&EN

Open-Source Machine Learning in Computational Chemistry

Institute of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany Department of Electrical Engineering, ...
eLife

Neural assemblies uncovered by generative modeling explain whole-brain activity statistics and reflect structural connectivity

Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet, it remains to be ...
GitHub

COVID-Net Open Source Initiative

Update 06/02/2022: We released COVIDx CXR-3, a cleaned version of the dataset in which several hundred bad training images have been removed. The new dataset contains 29,986 images from 16,648 ...
GitHub

Neural networks for deep mutational scanning data

We trained and evaluated the performance of multiple types of neural networks on five deep mutational scanning datasets. This repository contains code and examples that allow you to do the following: ...
eLife

DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning

Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the ...
Frontiers

Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification

For the past several decades, research in understanding the molecular basis of human muscle aging has progressed significantly. However, the development of accessible tissue-specific biomarkers of ...
C&EN

Automatic Differentiation in Quantum Chemistry with Applications to Fully Variational Hartree–Fock

Automatic differentiation (AD) is a powerful tool that allows calculating derivatives of implemented algorithms with respect to all of their parameters up to machine precision, without the need to ...