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APPROXIMATE BAYESIAN INFERENCE OF DIRECTED ACYCLIC GRAPHS IN biology WITH FLEXIBLE PRIORS ON EDGE STATES

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arXiv 2019年

作者： Martin, Evan A. Fu, Audrey Qiuyan The Bioinformatics and Computational Biology Program University of Idaho United States The Bioinformatics and Computational Biology Program Department of Mathematics and Statistical Science Institute for Modeling Collaboration and Innovation Institute for Interdisciplinary Data Sciences University of Idaho United States

Graphical models or networks describe the statistical dependence among multiple variables and are widely used in biology (e.g., gene regulatory networks). Under appropriate assumptions, directed edges may represent causal relationships. A key feature of a biological network is sparsity, defined by how likely an edge is present, of which we often have some knowledge. However, most existing Bayesian methods use priors for the entire graph, making it difficult to specify the level of sparsity. The few methods that use priors on edges estimate the two directions independently;the sum of the two probabilities can exceed 1. Here, we present baycn (BAYesian Causal Network), a novel approximate Bayesian method that represents a graph in terms of three states of edges: the two directions and edge absence, and specifies priors on these edge states. We design a pseudo Bayesian sampling algorithm for efficient inference. We apply baycn to two genomic problems: i) distinguishing direct and indirect target genes of genetic variants, using these variants as instrumental variables, and ii) inferring combinatorial binding of highly-correlated transcription factors in Drosophila. In both cases and in extensive simulations, our method demonstrates much improved accuracy over existing methods for the whole graph and for individual edges. Copyright © 2019, The Authors. All rights reserved.

关键词： Inference engines

Evaluating Methods for the Prediction of Cell Type-Specific Enhancers in the Mammalian Cortex

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SSRN

SSRN 2024年

作者： Johansen, Nelson J. Kempynck, Niklas Zemke, Nathan R. Somasundaram, Saroja Winter, Seppe De Hooper, Marcus Dwivedi, Deepanjali Lohia, Ruchi Wehbe, Fabien Li, Bocheng Abaffyová, Darina Armand, Ethan J. Man, Julie De Eksi, Eren Can Hecker, Nikolai Hulselmans, Gert Konstantakos, Vasilis Mauduit, David Mich, John K. Partel, Gabriele Daigle, Tanya L. Levi, Boaz P. Zhang, Kai Tanaka, Yoshiaki Gillis, Jesse Ting, Jonathan T. Ben-Simon, Yoav Miller, Jeremy Ecker, Joseph R. Ren, Bing Aerts, Stein Lein, Ed S. Tasic, Bosiljka Bakken, Trygve E. Allen Institute for Brain Science SeattleWA98109 United States VIB Center for AI & Computational Biology VIB-KU Leuven Center for Brain and Disease Research KU Leuven Department of Human Genetics Leuven Belgium Center for Epigenomics Department of Cellular and Molecular Medicine University of California La Jolla San DiegoCA92093 United States Physiology Department Donnelly Centre for Cellular and Biomolecular Research University of Toronto TorontoON Canada Maisonneuve-Rosemont Hospital Research Centre University of Montreal MontrealQC Canada School of Life Sciences Westlake University Zhejiang Hangzhou China Bioinformatics and Systems Biology Program University of California La Jolla San DiegoCA92093 United States Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Physiology and Biophysics University of Washington SeattleWA98195 United States Salk Institute for Biological Studies La Jolla CA92037 United States

Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet the mammalian brain. We organized the "Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi- Omics" to assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on in vivo validation data from hundreds of cortical cell type-specific enhancers that were previously packaged into individual AAV vectors and retro-orbitally injected into mice. We find that open chromatin was a key predictor of functional enhancers, and sequence models improved prediction of non-functional enhancers that can be deprioritized as opposed to pursued for in vivo testing. Sequence models also identified cell type-specific transcription factor codes that can guide designs of in silico enhancers. This community challenge establishes a benchmark for enhancer prioritization algorithms and reveals computational approaches and molecular information that are crucial for the identification of functional enhancers for mammalian cortical cell types. The results of this challenge bring us closer to understanding the complex gene regulatory landscape of the mammalian brain and help us design more efficient genetic tools and potential gene therapies for human neurological diseases. © 2024, The Authors. All rights reserved.

关键词： Mammals

Sorbate induces lysine sorbylation through noncanonical activities of class I HDACs to regulate the expression of inflammation genes

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Science advances 2025年第22期11卷 eadv1071页

作者： Yi-Cheng Sin Breann Abernathy Zuo-Fei Yuan Jason L Heier Justin E Gonzalez Laurie L Parker Douglas G Mashek Yue Chen Department of Biochemistry Molecular Biology and Biophysics University of Minnesota Twin Cities Minneapolis MN USA. Bioinformatics and Computational Biology Program University of Minnesota Twin Cities Minneapolis MN USA. Center for Proteomics and Metabolomics St. Jude Children's Research Hospital Memphis TN USA. Department of Medicine Division of Diabetes Endocrinology and Metabolism University of Minnesota Twin Cities Minneapolis MN USA. Institute for the Biology of Aging and Metabolism University of Minnesota Twin Cities Minneapolis MN USA.

Environmental factors may affect gene expression through epigenetic modifications of histones and transcription factors. Here, we report that cellular uptake of sorbate, a common food preservative, induces lysine sorbylation (Ksor) in mammalian cells and tissue mediated by the noncanonical activities of class I histone deacetylases (HDAC1-3). We demonstrated that HDAC1-3 catalyze sorbylation upon sorbate uptake and desorbylation in the absence of sorbate both in vitro and in cells. Sorbate uptake in mice livers significantly induced histone Ksor, correlating with decreased expressions of inflammation-response genes. Accordingly, sorbate treatment in macrophage RAW264.7 cells upon lipopolysaccharide (LPS) stimulation dose-dependently down-regulated proinflammatory gene expressions and nitric oxide production. Proteomic profiling identified RelA, a component of the NF-κB complex, and its interacting proteins as bona fide Ksor targets and sorbate treatment significantly decreased NF-κB transcriptional activities in response to LPS stimulation in RAW264.7 cells. Together, our study demonstrated a noncanonical mechanism of sorbate uptake in regulating epigenetic histone modifications and inflammatory gene expression.

关键词： Transcription

Negative feedback equalizes polarity sites in a multi-budding yeast

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Current biology : CB 2025年

作者： Alex W Crocker Claudia A Petrucco Kaiyun Guan Alison C E Wirshing Joanne L Ekena Daniel J Lew Timothy C Elston Amy S Gladfelter Curriculum in Bioinformatics and Computational Biology University of North Carolina at Chapel Hill 120 Mason Farm Rd. Chapel Hill NC 27599 USA Duke University Department of Cell Biology 308 Research Drive Durham NC 27705 USA. Curriculum in Bioinformatics and Computational Biology University of North Carolina at Chapel Hill 120 Mason Farm Rd. Chapel Hill NC 27599 USA. Department of Biology Massachusetts Institute of Technology 31 Ames St. Cambridge MA 02139 USA. Department of Pharmacology University of North Carolina at Chapel Hill 120 Mason Farm Rd. Chapel Hill NC 27599 USA Computational Medicine Program University of North Carolina at Chapel Hill 116 Manning Drive Chapel Hill NC 27599 USA. Electronic address: telston@med.unc.edu. Duke University Department of Cell Biology 308 Research Drive Durham NC 27705 USA. Electronic address: amy.gladfelter@duke.edu.

Morphogenesis in fungi and animals is directed by the polarization of the small GTPases Cdc42 and Rac. In the budding yeast Saccharomyces cerevisiae, competition between polarity patches results in one polarized patch and the growth of a single bud. Here, we describe cell polarity in the yeast Aureobasidium pullulans, which establishes multiple coexisting polarity sites yielding multiple buds during a single cell-division cycle. Polarity machinery components oscillate in their abundance in these coexisting sites but do so independently of one another, pointing to a lack of global coupling between sites. Previous theoretical work has demonstrated that negative feedback in a polarity circuit could promote the coexistence of multiple polarity sites, and time-delayed negative feedback is known to cause oscillations. We show that both these features of negative feedback depend on a protein we identified as Pak1 and that Pak1 requires Rac1 but not Cdc42 for its localization. This work shows how conserved signaling networks can be modulated for distinct morphogenic programs even within the constraints of fungal budding.

关键词： Cdc42 Pak1 extremotolerant yeasts multi-budding polarity

Improved Prediction Of Ligand-Protein Binding Affinities By Meta-Modeling

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arXiv 2023年

作者： Lee, Ho-Joon Emani, Prashant S. Gerstein, Mark B. Dept. of Genetics and Yale University New HavenCT06510 United States Yale Center for Genome Analysis Yale University New HavenCT06510 United States Dept. of Molecular Biophysics & Biochemistry Yale University New HavenCT06520 United States Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Dept. of Computer Science Yale University New HavenCT06520 United States Dept. of Statistics & Data Science Yale University New HavenCT06520 United States Dept. of Biomedical Informatics & Data Science Yale University New HavenCT06520 United States

The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between ligands and proteins. Many computational models for binding affinity prediction have been developed, but with varying results across targets. Given that ensembling or meta-modeling approaches have shown great promise in reducing model-specific biases, we develop a framework to integrate published force-field-based empirical docking and sequence-based deep learning models. In building this framework, we evaluate many combinations of individual base models, training databases, and several meta-modeling approaches. We show that many of our meta-models significantly improve affinity predictions over base models. Our best meta-models achieve comparable performance to state-of-the-art deep learning tools exclusively based on 3D structures, while allowing for improved database scalability and flexibility through the explicit inclusion of features such as physicochemical properties or molecular descriptors. We further demonstrate improved generalization capability by our models using a large-scale benchmark of affinity prediction as well as a virtual screening application benchmark. Overall, we demonstrate that diverse modeling approaches can be ensembled together to gain meaningful improvement in binding affinity prediction. © 2023, CC BY-NC-SA.

关键词： Prediction models

Vertex-Frequency Clustering

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Vertex-Frequency Clustering

IEEE Data Science Workshop (DSW)

作者： Daniel B. Burkhardt Jay S. Stanley III Guy Wolf Smita Krishnaswamy Department of Genetics Computational Biology and Bioinformatics Program Department of Mathematics and Statistics Université de Montréal

We propose a novel approach for clustering the vertices of a graph. The method, Vertex-Frequency Clustering (VFC), considers the local harmonic content of one or many graph signals, forming partitions based on spectral features in the input signal. The method can be related to spectral clustering, and the length scale over which frequencies are considered is tunable. This allows one to cluster data based on intrinsic graph geometry in the context of signal dynamics. VFC is useful for unravelling active regions in a signal, collecting sets of similar observations, or detecting anomalies. We demonstrate the utility of VFC in synthetic and biological data, and show how VFC can be used to identify observations with similar feature sets and signal profiles.

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Hopper flows of deformable particles

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arXiv 2022年

作者： Cheng, Yuxuan Treado, John D. Lonial, Ben Habdas, Piotr Weeks, Eric R. Shattuck, Mark D. O'Hern, Corey S. Department of Physics Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Physics Emory University AtlantaGA30322 United States Department of Physics Saint Joseph's University PhiladelphiaPA19131 United States Benjamin Levich Institute Physics Department The City College of New York New YorkNY10031 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Numerous experimental and computational studies show that continuous hopper flows of granular materials obey the Beverloo equation that relates the volume flow rate Q and the orifice width w: Q ∼ (w/σavg − k)β, where σavg is the average particle diameter, kσavg is an offset where Q ∼ 0, the power-law scaling exponent β = d − 1/2, and d is the spatial dimension. Recent studies of hopper flows of deformable particles in different background fluids suggest that the particle stiffness and dissipation mechanism can also strongly affect the power-law scaling exponent β. We carry out computational studies of hopper flows of deformable particles with both kinetic friction and background fluid dissipation in two and three dimensions. We show that the exponent β varies continuously with the ratio of the viscous drag to the kinetic friction coefficient, λ = ζ/µ. β = d−1/2 in the λ → 0 limit and d− 3/2 in the λ → ∞ limit, with a midpoint λc that depends on the hopper opening angle θw. We also characterize the spatial structure of the flows and associate changes in spatial structure of the hopper flows to changes in the exponent β. The offset k increases with particle stiffness until k ∼ kmax in the hard-particle limit, where kmax ∼ 3.5 is larger for λ → ∞ compared to that for λ → 0. Finally, we show that the simulations of hopper flows of deformable particles in the λ → ∞ limit recapitulate the experimental results for quasi-2D hopper flows of oil droplets in water. © 2022, CC BY.

关键词： Stiffness

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Cover Feature: Diversity Oriented Strategy (DOS) for the Efficient Synthesis of Benzofuro[2,3-b]pyridine Derivatives with Anticancer Activity (ChemMedChem 3/2025)

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ChemMedChem 2025年第3期20卷

作者： Reymark Ereje Jantana Yahuafai Theeranuch Jaroenchuensiri Patcharaporn Supakijjanusorn Sukanya Unson Borwornlak Toopradab Thanyada Rungrotmongkol Somsak Pianwanit Chanat Aonbangkhen Tanatorn Khotavivattana Center of Excellence in Natural Products Department of Chemistry Faculty of Science Chulalongkorn University Pathumwan Bangkok 10330 Thailand Department of Physical Sciences College of Science University of the Philippines Baguio Baguio City 2600 Philippines Clinical Research Section Division of Research and Academic Support National Cancer Institute Bangkok 10400 Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Pathumwan Bangkok 10330 Thailand Center of Excellence in Computational Chemistry Department of Chemistry Faculty of Science Chulalongkorn University Pathumwan Bangkok 10330 Thailand