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A technique that leverages duplicate records in crowdsourcing data could help to mitigate the effects of biases in research and services that are dependent on government records.
EmerGNN is a flow-based graph neural network (GNN) approach that advances on conventional methodologies for predicting drug–drug interactions in emerging drugs by effectively leveraging biomedical networks.
It is difficult to identify stable surface reconstructions of complex materials. Now a Monte Carlo sampling strategy is coupled with a machine learning interatomic potential that is iteratively improved via active learning during the search.
Enzymatic pathways control a host of cellular processes, but the complexity of such pathways has made them difficult to predict. Elektrum combines neural architecture search, kinetic models and transfer learning to effectively discover CRISPR–Cas9 cleavage kinetics.
The visualization and analysis of biological events using fluorescence microscopy is limited by the noise inherent in the images obtained. Now, a self-supervised spatial redundancy denoising transformer is proposed to address this challenge.
A recent study presents an approach for characterizing and quantifying the pore space in assemblies of particles, enabling research into pore-scale flow physics and insight into the interplay between the solid and void phases in granular materials.
The accurate prediction of molecular spectra is essential for substance discovery and structure identification, but conventional quantum chemistry methods are computationally expensive. Now, DetaNet achieves the accuracy of quantum chemistry while improving the efficiency of prediction of organic molecular spectra.
Programmability is crucial in noisy intermediate-scale quantum computing, facilitating various functionalities for practical applications. An arbitrary programmable time-bin-encoded quantum boson sampling device has been developed, specifically tailored for potential drug discovery.
A guided diffusion model pushes the boundaries of de novo molecular design, extensively exploring the chemical space and generating chemical compounds that satisfy custom target criteria.
A recent study proposes a unified framework that can compare different measures for quantifying the statistics of pairwise interactions in data from complex dynamical systems.
Deep learning approaches have potential to substantially reduce the astronomical costs and long timescales involved in drug discovery. KarmaDock proposes a deep learning workflow for ligand docking that shows improved performance against both benchmark cases and in a real-world virtual screening experiment.
A reinforcement-learning-based framework is proposed for assisting urban planners in the complex task of optimizing the spatial design of urban communities.
A hierarchical Bayesian method identifies cell-type specific changes in gene regulatory circuits in disease by integrating single-cell and three-dimensional measurements of the genome.
A recent work introduces a cellular deconvolution method, MeDuSA, of estimating cell-state abundance along a one-dimensional trajectory from bulk RNA-seq data with fine resolution and high accuracy, enabling the characterization of cell-state transition in various biological processes.
Real-time mobility data capturing city-wide human movement can be used to characterize cities, their segregation, and population responses to exogenous events such as pandemics.
Deep learning is used to accelerate the inference of genetic clusters, allowing the analysis of hundreds of thousands of human genomic datasets in a computationally efficient way.
A momentum-space algorithm is proposed to simulate electron dynamics with time-dependent density functional theory, which expands the scope of conventional real-space methods.
