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Simulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than established molecular mechanics (MM) force fields. Here, we propose Grappa, a machine learning framework to predict MM parameters from the molecular graph, employing a graph attentional neural network and a transformer with symmetry-preserving positional encoding.
View Article and Find Full Text PDFGenome-wide association studies are enriched for interacting genes.
BioData Min
January 2025
The Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90069, USA.
Background: With recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk.
View Article and Find Full Text PDFLampbrush chromosomes of Danio rerio.
Chromosome Res
January 2025
Saint-Petersburg State University, Saint-Petersburg, Russia.
Danio rerio, commonly known as zebrafish, is an established model organism for the developmental and cell biology studies. Although significant progress has been made in the analysis of the D. rerio genome, cytogenetic studies face challenges due to the unclear identification of chromosomes.
View Article and Find Full Text PDFIdentifying Disease Associated Multi-Omics Network With Mixed Graphical Models Based on Markov Random Field Model.
Genet Epidemiol
January 2025
Interdisciplinary Program of Bioinformatics, College of Natural Science, Seoul National University, Seoul, South Korea.
In this article, we proposed a new method named fused mixed graphical model (FMGM), which can infer network structures associated with dichotomous phenotypes. FMGM is based on a pairwise Markov random field model, and statistical analyses including the proposed method were conducted to find biological markers and underlying network structures of the atopic dermatitis (AD) from multiomics data of 6-month-old infants. The performance of FMGM was evaluated with simulations by using synthetic datasets of power-law networks, showing that FMGM had superior performance for identifying the differences of the networks compared to the separate inference with the previous method, causalMGM (F1-scores 0.
View Article and Find Full Text PDFSingle Extracellular VEsicle Nanoscopy-Universal Protocol (SEVEN-UP): Accessible Imaging Platform for Quantitative Characterization of Single Extracellular Vesicles.
Anal Chem
January 2025
Department of Cancer Biology and Molecular Medicine, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, California 91010, United States.
Extracellular vesicles (EVs), membrane-encapsulated nanoparticles shed from all cells, are tightly involved in critical cellular functions. Moreover, EVs have recently emerged as exciting therapeutic modalities, delivery vectors, and biomarker sources. However, EVs are difficult to characterize, because they are typically small and heterogeneous in size, origin, and molecular content.
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