Volume 3 Issue 7, July 2023

The prediction of stable crystal structures is an important part of designing solid-state crystalline materials with desired properties. Recent advances in structural feature representations and generative neural networks promise the ability to efficiently create new stable structures to use for inverse design and to search for materials with tailored functionalities.

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 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.

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.

GRAPE is a software resource for graph processing, learning and embedding that is orders of magnitude faster than existing state-of-the-art libraries. GRAPE can quickly process real-world graphs with millions of nodes and billions of edges, enabling complex graph analyses and research in graph-based machine learning and in diverse disciplines.

The field of human mobility has evolved drastically in the past 20 years. In this Perspective, the authors discuss three key areas in human mobility, framed as minds, societies and algorithms, where they expect to see substantial improvements in the future.

While the adherence to fairness constraints has become common practice in the design of algorithms across many contexts, a more holistic approach should be taken to avoid inflicting additional burdens on individuals in all groups, including those in marginalized communities.

The study presents a mobility centrality index to delineate urban dynamics in quasi-real time with mobile-phone data. The results indicate that urban structures were becoming more monocentric during the COVID-19 lockdown periods in major cities in Spain.

Neural ADMIXTURE is a neural-network-based, interpretable autoencoder that performs rapid genomic clustering in biobank-scale databases.

MeDuSA, a mixed model-based method, leverages single-cell RNA-sequencing data for high-accuracy, fine-resolution cellular deconvolution in bulk RNA-sequencing data. It improves deconvolution accuracy over existing methods, revealing cell-state dynamics in various biological processes.

A Bayesian method, called MAGICAL, that contrasts single cell multiomics data across conditions to accurately discover differences in gene regulatory circuits at cell type resolution is applied to specific host-based diagnosis of bacterial sepsis.