检索结果-内蒙古大学图书馆

KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection

Science (New York, N.Y.) 2025年第6735期387卷 eadn2337页

作者： Fagerberg, Eric Attanasio, John Dien, Christine Singh, Jaiveer Kessler, Emily A. Abdullah, Leena Shen, Jian Hunt, Brian G. Connolly, Kelli A. De Brouwer, Edward He, Jiaming Iyer, Nivedita R. Buck, Jessica Borr, Emily R. Damo, Martina Foster, Gena G. Giles, Josephine R. Huang, Yina H. Tsang, John S. Krishnaswamy, Smita Cui, Weiguo Joshi, Nikhil S. Department of Immunobiology Yale University School of Medicine New Haven CT United States Program in Computational Biology & Bioinformatics Yale University New Haven CT United States Department of Microbiology and Immunology Geisel School of Medicine at Dartmouth NH United States Department of Pathology Feinberg School of Medicine at Northwestern University Chicago IL United States Department of Genetics and Computer Science Yale University School of Medicine New Haven CT United States Department of Medicine University of Chicago Chicago IL United States Section of Hematology Department of Internal Medicine Yale University School of Medicine New Haven CT United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Philadelphia PA United States Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Parker Institute for Cancer Immunotherapy Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Yale Center for Systems and Engineering Immunology Yale University School of Medicine New Haven CT United States New Haven CT United States Department of Biomedical Engineering Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University CT USA New Haven United States Applied Math Program Yale University New Haven CT United States

Naïve CD8 T cells have the potential to differentiate into a spectrum of functional states during an immune response. How these developmental decisions are made and what mechanisms exist to suppress differentiation toward alternative fates remains unclear. We employed in vivo CRISPR-Cas9-based perturbation sequencing to assess the role of ~40 transcription factors (TFs) and epigenetic modulators in T cell fate decisions. Unexpectedly, we found that knockout of the TF Klf2 resulted in aberrant differentiation to exhausted-like CD8 T cells during acute infection. KLF2 was required to suppress the exhaustion-promoting TF TOX and to enable the TF TBET to drive effector differentiation. KLF2 was also necessary to maintain a polyfunctional tumor-specific progenitor state. Thus, KLF2 provides effector CD8 T cell lineage fidelity and suppresses the exhaustion program.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Variable importance-weighted Random Forests

引用

Frontiers of Electrical and Electronic Engineering in China 2017年第4期5卷 338-351页

作者： Yiyi Liu Hongyu Zhao Department of Biostatistics School of Public Health Yale University New Haven CT 06511 USA Program of Computational Biology and Bioinformatics Yale University New Haven CT 06511 USA

Background： Random Forests is a popular classification and regression method that has proven powerful for various prediction problems in biological studies. However, its performance often deteriorates when the number of features increases. To address this limitation, feature elimination Random Forests was proposed that only uses features with the largest variable importance scores. Yet the performance of this method is not satisfying, possibly due to its rigid feature selection, and increased correlations between trees of forest. Methods： We propose variable importance-weighted Random Forests, which instead of sampling features with equal probability at each node to build up trees, samples features according to their variable importance scores, and then select the best split from the randomly selected features. Results： We evaluate the performance of our method through comprehensive simulation and real data analyses, for both regression and classification. Compared to the standard Random Forests and the feature elimination Random Forests methods, our proposed method has improved performance in most cases. Conclusions： By incorporating the variable importance scores into the random feature selection step, our method can better utilize more informative features without completely ignoring less informative ones, hence has improved prediction accuracy in the presence of weak signals and large noises. We have implemented an R package ＂viRandomForests＂ based on the original R package ＂randomForest＂ and it can be freely downloaded from http：// ***/software.

关键词： Random Forests variable importance score classification regression

来源：评论

学校读者我要写书评

暂无评论

B-cell Ligand Processing Pathways Detected by Large-scale Comparative Analysis

引用

Genomics, Proteomics & bioinformatics 2012年第3期10卷 142-152页

作者： Fadi Towfic Shakti Gupta Vasant Honavar Shankar Subramaniam Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIA 50010USA Department of Bioengineering University of California at San DiegoLa JollaCA 92122USA

The initiation of B-cell ligand recognition is a critical step for the generation of an immune response against foreign bodies. We sought to identify the biochemical pathways involved in the B-cell ligand recognition cascade and sets of ligands that trigger similar immunological responses. We utilized several comparative approaches to analyze the gene coexpression networks generated from a set of microarray experiments spanning 33 different ligands. First, we compared the degree distributions of the generated networks. Second, we utilized a pairwise network alignment algorithm, BiNA, to align the networks based on the hubs in the networks. Third, we aligned the networks based on a set of KEGG pathways. We summarized our results by constructing a consensus hierarchy of pathways that are involved in B cell ligand recognition. The resulting pathways were further validated through literature for their common physiological responses. Col- lectively, the results based on our comparative analyses of degree distributions, alignment of hubs, and alignment based on KEGG path- ways provide a basis for molecular characterization of the immune response states of B-cells and demonstrate the power of comparative approaches （e.g., gene coexpression network alignment algorithms） in elucidating biochemical pathways involved in complex signaling events in ceils.

关键词： Ligand recognition B-cells Gene coexpression network alignment

来源：评论

学校读者我要写书评

暂无评论

Mapping Emergency Medicine Data to the Observational Medical Outcomes Partnership Common Data Model: A Gap Analysis of the American College of Emergency Physicians Clinical Emergency Data Registry

引用

JACEP Open 2025年第1期6卷 100016-100016页

作者： Cohen, Inessa Diao, Zihan Goyal, Pawan Gupta, Aarti Hawk, Kathryn Malcom, Bill Malicki, Caitlin Sharma, Dhruv Sweeney, Brian Weiner, Scott G. Venkatesh, Arjun Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States American College of Emergency Physicians Washington DC United States Department of Emergency Medicine Brigham and Women's Hospital Boston MA United States Center for Outcomes Research and Evaluation (CORE) Section of Cardiovascular Medicine Yale School of Medicine New Haven CT United States

Objectives: This study aims to conduct a gap analysis to determine the feasibility of mapping electronic health record data from the Clinical Emergency Data Registry (CEDR) to the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM). Methods: We employed a structured approach using a custom-built comparison matrix. This matrix facilitated the alignment of CEDR data fields with the corresponding elements in the OMOP-CDM schema. Each field was evaluated for compatibility, with categorization into 3 distinct types: direct matches, fields requiring transformation, and fields with no OMOP-CDM equivalent. The mapping process was informed by consultations with the Observational Health Data Sciences and Informatics community forums and was guided by existing documentation and best practices in data harmonization. We performed descriptive analyses, quantifying the extent of direct matches and identifying the specific transformations needed for each CEDR-CDM field to ensure compliance with the OMOP-CDM model. Results: Our analysis indicates a high degree of compatibility between CEDR and OMOP, with over 90% (244/269) of CEDR fields being successfully mapped. Specifically, 173 fields had direct matches, whereas 71 required transformations. Challenges identified include addressing fields unique to CEDR with no OMOP-CDM equivalent and managing the transformations required for proper alignment. Conclusion: The OMOP-CDM presents a promising framework for standardizing emergency medicine data, thereby enhancing future query automation, analytics, and cross-institutional collaboration. Despite the potential challenges in capturing unique CEDR fields and addressing necessary transformations, most emergency department data can be standardized within the OMOP-CDM, fostering broader insights and applications in research and public health. © 2024 The Author(s)

关键词： databases electronic health records emergency service informatics opioid/adverse effects quality of health care registries

来源：评论

学校读者我要写书评

暂无评论

Phytohormone signaling pathway analysis method for comparing hormone responses in plant-pest interactions

引用

BMC Research Notes 2012年第1期5卷 1-7页

作者： Studham, Matthew E. MacIntosh, Gustavo C. Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States

Background: Phytohormones mediate plant defense responses to pests and pathogens. In particular, the hormones jasmonic acid, ethylene, salicylic acid, and abscisic acid have been shown to dictate and fine-tune defense responses, and identification of the phytohormone components of a particular defense response is commonly used to characterize it. Identification of phytohormone regulation is particularly important in transcriptome analyses. Currently there is no computational tool to determine the relative activity of these hormones that can be applied to transcriptome analyses in soybean. Findings: We developed a pathway analysis method that provides a broad measure of the activation or suppression of individual phytohormone pathways based on changes in transcript expression of pathway-related genes. The magnitude and significance of these changes are used to determine a pathway score for a phytohormone for a given comparison in a microarray experiment. Scores for individual hormones can then be compared to determine the dominant phytohormone in a given defense response. To validate this method, it was applied to publicly available data from previous microarray experiments that studied the response of soybean plants to Asian soybean rust and soybean cyst nematode. The results of the analyses for these experiments agreed with our current understanding of the role of phytohormones in these defense responses. Conclusions: This method is useful in providing a broad measure of the relative induction and suppression of soybean phytohormones during a defense response. This method could be used as part of microarray studies that include individual transcript analysis, gene set analysis, and other methods for a comprehensive defense response characterization. © 2012 Studham and MacIntosh;licensee BioMed Central Ltd.

关键词： Salicylic Acid Jasmonic Acid Soybean Cyst Nematode Soybean Aphid Salicylic Acid Pathway

来源：评论

学校读者我要写书评

暂无评论

An analysis of conformational changes upon RNA-Protein binding 14

An analysis of conformational changes upon RNA-Protein bindi...

引用

5th ACM Conference on bioinformatics, computational biology, and Health Informatics, ACM BCB 2014

作者： Sankar, Kannan Jernigan, Robert L. Walia, Rasna R. Honavar, Vasant G. Mann, Carla M. Dobbs, Drena Bioinformatics and Computational Biology Program Dept. of Biochem. Biophysics and Molecular Biology Iowa State University United States Dept. of Biochem. L.H. Baker Center for Bioinformatics and Biological Statistics Iowa State University United States Bioinformatics and Computational Biology Program Dept. of Computer Science Iowa State University United States Bioinformatics and Genomics Program College of Information Sciences and Technology Pennsylvania State University United States Dept. of Genetics Development and Cell Biology L.H. Baker Center for Bioinformatics Iowa State University United States

ISBN: (纸本)9781450328944

RNA-binding proteins (RBPs) have myriad functions in transcription, translation, and post-transcriptional gene regulation, with central roles in normal development as well as in both genetic and infectious diseases. When a protein binds RNA, a conformational change often occurs. For RNA-protein complexes that have been characterized, conformational changes have been observed in the protein, the RNA, or both. These conformational changes have not been sufficiently characterized, however, in part due to the small number of structures of bound and unbound complexes of RNA-binding proteins available until recently. Here we systematically analyze a new dataset of 90 pairs of bound and unbound proteins to evaluate the conformational changes that occur upon RNA binding. Most of the conformational changes were observed in noninterfacial regions of the RNA-binding proteins. Detailed analyses of the modes of RNA binding and any associated conformational changes in proteins are critical for fully understanding the mechanisms of RNA-protein recognition, for developing better RNA-protein docking methods and methods for predicting interfacial residues, and for RNA-based drug design. Copyright © 2014 ACM.

关键词： RNA

来源：评论

学校读者我要写书评

暂无评论

A Comparison of Lossless Compression Methods in Microscopy Data Storage Applications: Microscopy compression comparison 23

A Comparison of Lossless Compression Methods in Microscopy D...

引用

6th International Conference on Software Engineering and Information Management, ICSIM 2023

作者： Walker, Logan Alexander Li, Ye McGlothlin, Maggie Cai, Dawen University of Michigan Biophysics Program Department of Computational Medicine and Bioinformatics United States University of Michigan Cell and Developmental Biology United States University of Michigan Cell and Developmental Biology Biophysics Program Neuroscience United States

ISBN: (纸本)9781450398237

Modern high-throughput microscopy methods such as light-sheet imaging and electron microscopy can produce petabytes of data inside of a single experiment. Storage of these large images, however, is challenging because of the difficulty of moving, storing, and analyzing such vast amounts of data, which is often collected at very high data rates (>1GBps). In this report, we provide a comparison of the performance of several compression algorithms using a collection of published and unpublished datasets including confocal, fMOST, and pathology images. We also use simulated data to demonstrate the efficiency of each algorithm as image content or entropy increases. As a result of this work, we recommend the use of the BLOSC algorithm combined with ZSTD for various microscopy applications, as it produces the best compression ratio over a collection of conditions. © 2023 ACM.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

BPS2025 - Cross-scale conundrum: Slow masticatory myosin in jaw muscles that are both fast and strong

引用

Biophysical Journal 2025年第3期124卷 516a-516a页

作者： Varun V. Dhorajia Nicholas LaFave Brandon Reder Robert Brocklehurst Nicolai Konow Sam Walcott Jeffrey R. Moore Biomedical Engineering and Biotechnology Program University of Massachusetts Lowell Lowell MA USA Department of Biological Sciences University of Massachusetts Lowell Lowell MA USA Department of Mathematical Sciences Worcester Polytechnic Institute Worcester MA USA Department of Bioinformatics and Computational Biology Worcester Polytechnic Institute Worcester MA USA

来源：评论

学校读者我要写书评

暂无评论

Predicting dynamic cellular protein-RNA interactions by deep learning using in vivo RNA structures

引用

细胞研究（英文版） 2021年第5期31卷 495-516页

作者： Lei Sun Kui Xu Wenze Huang Yucheng T.Yang Pan Li Lei Tang Tuanlin Xiong Qiangfeng Cliff Zhang MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological StructureCenter for Synthetic and Systems BiologySchool of Life SciencesTsinghua UniversityBeijing 100084China Tsinghua-Peking Center for Life Sciences Beijing 100084China Program in Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06520USA Department of Molecular Biophysics and Biochemistry Yale UniversityNew HavenCT 06520USA

Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation,and dynamically reflect specific cellular ***,presently available tools for predicting RBP-RNA interactions employ RNA sequence and/or predicted RNA structures,and therefore do not capture their condition-dependent ***,after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types,we developed PrismNet,a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular *** results for 168 RBPs support its utility for both understanding CUP-seq results and largely extending such interaction data to accurately analyze additional cell ***,PrismNet employs an 'attention' strategy to computationally identify exact RBP-binding nucleotides,and we discovered enrichment among dynamic RBP-binding sites for structure-changing variants(riboSNitches),which can link genetic diseases with dysregulated RBP *** rich profiling data and deep learning-based prediction tool provide access to a previously inaccessible layer of cell-type-specific RBP-RNA interactions,with clear utility for understanding and treating human diseases.

关键词： bioinformatics Mechanisms of disease Molecular biology

来源：评论

学校读者我要写书评

暂无评论

Biomedical data and AI

引用

Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：