Nature computational science最新文献

The synthesis of crystalline materials, such as zeolites, remains a notable challenge owing to a high-dimensional synthesis space, intricate structure-synthesis relationships and time-consuming experiments. Here, considering the 'one-to-many' relationship between structure and synthesis, we propose DiffSyn, a generative diffusion model trained on over 23,000 synthesis recipes that span 50 years of literature. DiffSyn generates probable synthesis routes conditioned on a desired zeolite structure and an organic template. DiffSyn a chieves state-of-the-art performance by capturing the multi-modal nature of structure-synthesis relationships. We apply Diffsny to differentiate among competing phases and generate optimal synthesis routes. As a proof of concept, we synthesize a UFI material using DiffSyn-generated synthesis routes. These routes, rationalized by density functional theory binding energies, resulted in the successful synthesis of a UFI material with a high Si/Al_ICP of 19.0, which is expected to improve thermal stability.

Accurately annotating cell types is essential for extracting biological insight from single-cell RNA sequencing data. Although cell types are naturally organized into hierarchical ontologies, most computational models do not explicitly incorporate this structure into their training objectives. Here, we introduce a hierarchical cross-entropy loss that aligns model objectives with biological structure. Applied to architectures ranging from linear models to transformers, this simple modification improves out-of-distribution performance by 12-15% without added computational cost. Critically, we underscore the need to focus on new data generation that improves the connectivity among annotated cell types. Our work suggests that this is likely to yield more generalizable algorithms than would solely increasing model complexity.

Voxel-based morphometry (VBM), a popular approach in neuroimaging research, uses magnetic resonance imaging data to assess variations in the local density of brain tissue and to examine its associations with biological and psychometric variables. Here we present deepmriprep, a preprocessing pipeline designed to leverage neural networks to perform all the necessary preprocessing steps for the VBM analysis of T₁-weighted magnetic resonance imaging. Utilizing the graphics processing unit, deepmriprep is 37 times faster than CAT12, the leading VBM preprocessing toolbox. The proposed method matches CAT12 in accuracy for tissue segmentation and image registration across more than 100 datasets and shows strong correlations in the VBM results. Tissue segmentation maps from deepmriprep have more than 95% agreement with ground-truth maps, and its nonlinear registration predicts smooth deformation fields comparable to CAT12. The high computational speed of deepmriprep enables rapid preprocessing of large datasets and opens the door to real-time applications.

Molecule generation is advancing rapidly in chemical discovery and drug design. Flow-matching methods have recently set the state of the art (SOTA) in unconditional molecule generation, surpassing score-based diffusion models. However, diffusion models still lead in property-guided generation. In this work, we introduce PropMolFlow, an approach for property-guided molecule generation based on geometry-complete SE(3)-equivariant flow matching. Integrating five different property embedding methods with a Gaussian expansion of scalar properties, PropMolFlow achieves competitive performance against previous SOTA diffusion models in conditional molecule generation while maintaining high structural stability and validity. Additionally, it enables higher sampling speed with fewer time steps compared with baseline models. We highlight the importance of validating the properties of generated molecules through density functional theory calculations. Furthermore, we introduce a task to assess the model's ability to propose molecules with under-represented property values, assessing its capacity for out-of-distribution generalization.

Spatial epigenomics (SE) technologies profile epigenomic landscapes within intact tissues, preserving spatial context and enabling the study of gene regulatory mechanisms in situ. However, current SE datasets typically suffer from low signal detection, substantial noise and extremely sparse peak matrices, which pose considerable challenges for downstream analysis. Here we introduce SPEED (spatial epigenomic data denoising), a deep matrix factorization framework that leverages atlas-level single-cell epigenomic data and spatial context to impute and denoise SE data. In comprehensive benchmarks on both simulated data and real SE tissue datasets, SPEED outperformed five state-of-the-art methods across diverse tissues and technologies. Moreover, SPEED's denoised outputs facilitated downstream analyses such as differential chromatin accessibility analysis, epigenomic spatial domain identification and gene activity inference. Collectively, our results indicate that SPEED is a generalizable tool for improving data quality and biological insights in SE.

Conventional epigenetic clocks encounter challenges in generalizability, especially when there are pronounced batch effects between the training and test datasets, restricting their clinical applicability for aging assessment. Here we present MAPLE, a robust computational framework for methylation age and disease-risk prediction through pairwise learning. MAPLE utilizes pairwise learning to discern the relative relationships between two DNA methylation profiles regarding age or disease risk. It effectively identifies aging- or disease-related biological signals while mitigating technical biases in the data. MAPLE outperforms five competing methods, achieving a median absolute error of 1.6 years across 31 benchmark tests from diverse studies, sequencing platforms, data preprocessing methods and tissue types. Furthermore, MAPLE performs well when assessing aging-related disease risk, with mean areas under the curve of 0.97 for disease identification and 0.85 for pre-disease status detection. Overall, we show that MAPLE has great potential for assessing epigenetic age and aging-related disease risk clinically.

Spatial transcriptomics has transformed the mapping of gene expression within intact tissues, yet current sequencing-based platforms are limited by coarse spot-level resolution and sparse sampling that leaves large interspot regions unmeasured. Here we introduce PanoSpace, a computational framework that integrates low-resolution spatial transcriptomics with high-resolution histology and matched single-cell RNA sequencing to reconstruct a continuous, single-cell-level map across entire tissue sections. Originally developed for tumors, PanoSpace accurately reconstructs cellular locations, cell identities and gene expression profiles, enabling detailed characterization of intracell-type heterogeneity and spatially organized cell-cell interactions. Application to breast and prostate cancers reveals complex cellular architectures and tumor microenvironment dynamics mediated by cancer-associated fibroblasts. Thanks to its modular design, PanoSpace can be seamlessly adapted to noncancerous tissues, as demonstrated by precise spatial reconstruction in mouse brain. Together, these results demonstrate that PanoSpace enables comprehensive spatial transcriptomic analysis and facilitates biological discovery.