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Zhi Liu et al. develop a method to measure disparities in reporting delays in urban crowdsourcing systems, uncovering socioeconomic disparities and providing actionable insights for interventions that enhance the efficiency and equity of city services.
LOVAMAP is an analysis software that accurately identifies 3D pores in packed particle systems by exploiting information about the particle configuration as a basis for segmentation. Using the software, the authors were able to uncover striking relationships between particle and pore properties.
A hybrid machine learning–physics model is developed that reduces simulation cost by two orders of magnitude while retaining high ab initio accuracy, to predict free-energy transition states for hydrogen combustion reactions.
A physics-informed deep learning model, PBCNet, is proposed for predicting the relative binding affinity of ligands in order to improve guiding structure-based drug lead optimization.
A graph attention neural network tool is introduced to integrate multiple spatial transcriptomics data from different individuals, technologies and developmental stages, enabling consensus spatial domain identification and three-dimensional tissue reconstruction.
SurfGen is a structure-based drug design approach that delves into topological and geometric deep learning techniques for interaction learning, echoing the classical lock-and-key model.
GaUDI is a guided diffusion method for the design of molecular structures that features a flexible and scalable target function and that achieves high validity of generated molecules.
This work unifies an interdisciplinary literature of over 230 computational methods for measuring interactions from complex systems, revealing previously unreported theoretical connections and demonstrating practical benefits of broad methodological comparison.
KarmaDock, a deep learning approach, is proposed to improve the speed, accuracy and pose quality of molecular docking and is validated on multiple datasets and a real-world virtual screening.
The computational platform u-signal3D defines a shape-invariant representation of the spatial scales of molecular organization at the cell surface to enable comparison and machine learning of signaling across morphologically diverse cell populations.
A graph-based artificial intelligence model for urban planning outperforms human-designed plans in objective metrics, offering an efficient and adaptable collaborative workflow for future sustainable cities.
Real-world social networks are often ephemeral and subject to exogenous restructuring. Q. Su et al. show that dynamic networks can foster cooperative behavior.
Batch effects pose great statistical challenges to the analysis of biomedical data. One approach to address batch effects is through sample remeasurement in each batch. In this work, the researchers developed a rigorous batch-effect correction procedure based on remeasured samples.
Automating materials’ defect predictions with graph neural networks, when coupled to first principles thermodynamic calculations, accelerates materials discovery for a variety of high-temperature, clean-energy applications.
This study introduces a physically boosted cooperative learning framework (PBCL) to reveal 2D atmospheric turbulence strength fields from turbulence-distorted infrared images. The PBCL is further demonstrated to inhibit adverse turbulence effects in images.
A system called ORFanage can analyze RNA-seq data to find novel protein variants and improve gene annotations. In addition, the method is fast and scalable, being able to filter out noise and thus greatly improving the quality of transcriptome assemblies.
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.
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.
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.