Reviews & Analysis

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 recent work provides a mathematical framework for studying how changes in network structure over time can influence cooperative behavior.

A recent study develops a statistical method of correcting batch effects by employing remeasured samples in highly confounded case–control studies.

A recent study proposes an approach for identifying open reading frames that uses reference annotation to improve the accuracy of the detection.

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.

A computational tool based on an additive approach and linear algebra has been developed together with a fabrication strategy for the systematic exploration of rigid-deployable, compact and reconfigurable kirigami patterns.

An image-inspired deep-learning model is developed to generate realistic de novo protein structures and scaffolds around functional sites, which helps the search for new structures and functions in protein engineering.

Two computational methods — one physics-based, and the other one deep-learning based — are proposed to enable the systematic investigation of magnetic order in moiré magnets from first principles.

Discovering biological patterns from omics data is challenging due to the high dimensionality of biological data. A computational framework is presented to more efficiently calculate correlations among omics features and to build networks by estimating important connections.

A biasing potential is derived from the uncertainty of a neural network ensemble and used to modify the potential energy surface in molecular dynamics simulations and facilitate the determination of underrepresented structural regions.

A topology-based approach is developed to better understand the relationship between protein structure and protein function.

A computational tool has been developed for the multiscale design of open disordered material systems, bridging network science, computational materials, and wave physics.

Inferring gene networks from discrete RNA counts across cells remains a complex problem. Following Bayesian non-parametrics, a computational framework is proposed to perform non-biased inference of transcription kinetics from single-cell RNA counting experiments.

A proposed density functional approximation (DFA) recommender outperforms the use of a single functional by selecting the optimal exchange-correlation functional for a given system.

A framework for generating and interpreting dynamic visualizations from traditional static dimensionality reduction visualization methods has been proposed in a recent study.