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| http://dx.doi.org/10.24272/j.issn.2095-8137.2021.409 | DOI Listing |
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DeepGOMeta for functional insights into microbial communities using deep learning-based protein function prediction.
Sci Rep
December 2024
KAUST Center of Excellence for Smart Health (KCSH), King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia.
Analyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robust de novo protein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Traditional prediction methods, dependent on homology and sequence similarity, often fail to predict functions for novel proteins and proteins without known homologs.
View Article and Find Full Text PDFMultivariate stochastic modeling for transcriptional dynamics with cell-specific latent time using SDEvelo.
Nat Commun
December 2024
School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen, China.
Recently, RNA velocity has driven a paradigmatic change in single-cell RNA sequencing (scRNA-seq) studies, allowing the reconstruction and prediction of directed trajectories in cell differentiation and state transitions. Most existing methods of dynamic modeling use ordinary differential equations (ODE) for individual genes without applying multivariate approaches. However, this modeling strategy inadequately captures the intrinsically stochastic nature of transcriptional dynamics governed by a cell-specific latent time across multiple genes, potentially leading to erroneous results.
View Article and Find Full Text PDFMSA clustering enhances AF-Multimer's ability to predict conformational landscapes of protein-protein interactions.
Bioinform Adv
December 2024
Laboratory of Experimental Biophysics, Center for Advanced Technologies, Tashkent, 100174, Uzbekistan.
Motivation: Understanding the conformational landscape of protein-ligand interactions is critical for elucidating the binding mechanisms that govern these interactions. Traditional methods like molecular dynamics (MD) simulations are computationally intensive, leading to a demand for more efficient approaches. This study explores how multiple sequence alignment (MSA) clustering enhance AF-Multimer's ability to predict conformational landscapes, particularly for proteins with multiple conformational states.
View Article and Find Full Text PDFEye pupils mirror information divergence in approximate inference.
Sci Rep
December 2024
Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, 4-1-1 Ogawahigashi-cho, Kodaira, Tokyo, 187-8553, Japan.
Pupil dilation is considered to track the arousal state linked to a wide range of cognitive processes. A recent article suggested the potential to unify findings in pupillometry studies based on an information theory framework and Bayesian methods. However, Bayesian methods become computationally intractable in many realistic situations.
View Article and Find Full Text PDFExploration of Fusion and Fission of Molecular Assemblies in Aqueous Solution through a Dynamic Clustering Approach.
J Phys Chem A
December 2024
State Key Laboratory of Mesoscience and Engineering, Institute of Process Engineering, Chinese Academy of Science s, Beijing 100190, China.
To understand the mechanism of self-assembly and to predict the evolutionary pattern of the fusion-fission system over a long period of time, studying the dynamics of these processes is of great significance. The trajectories from molecular dynamics (MD) simulations of self-assembly processes contain numerous latent fusion and fission events. To analyze the fusion and fission events from the simulated trajectory, in this article, a dynamic clustering approach was developed by comparing the changes of monomer composition within clusters over simulated time.
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