检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Baskaran, Vishal Athreya Ranek, Jolene Shan, Siyuan Stanley, Natalie Oliva, Junier B. Department of Computer Science UNC-Chapel Hill United States Computational Medicine Program Curriculum in Bioinformatics and Computational Biology UNC-Chapel Hill United States Computational Medicine Program Department of Computer Science UNC-Chapel Hill United States

Modern high-throughput single-cell immune profiling technologies, such as flow and mass cytometry and single-cell RNA sequencing can readily measure the expression of a large number of protein or gene features across the millions of cells in a multi-patient cohort. While bioinformatics approaches can be used to link immune cell heterogeneity to external variables of interest, such as, clinical outcome or experimental label, they often struggle to accommodate such a large number of profiled cells. To ease this computational burden, a limited number of cells are typically sketched or subsampled from each patient. However, existing sketching approaches fail to adequately subsample rare cells from rare cell-populations, or fail to preserve the true frequencies of particular immune cell-types. Here, we propose a novel sketching approach based on Kernel Herding that selects a limited subsample of all cells while preserving the underlying frequencies of immune cell-types. We tested our approach on three flow and mass cytometry datasets and on one single-cell RNA sequencing dataset and demonstrate that the sketched cells (1) more accurately represent the overall cellular landscape and (2) facilitate increased performance in downstream analysis tasks, such as classifying patients according to their clinical outcome. An implementation of sketching with Kernel Herding is publicly available at https://***/vishalathreya/Set-Summarization. © 2022, CC0.

关键词： bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Transparent Single-Cell Set Classification with Kernel Mean Embeddings

arXiv

引用

arXiv 2022年

作者： Shan, Siyuan Baskaran, Vishal Athreya Yi, Haidong Ranek, Jolene Stanley, Natalie Oliva, Junier B. Department of Computer Science UNC Chapel Hill United States Computational Medicine Program Curriculum in Bioinformatics and Computational Biology UNC Chapel Hill United States Computational Medicine Program Department of Computer Science UNC Chapel Hill United States

Modern single-cell flow and mass cytometry technologies measure the expression of several proteins of the individual cells within a blood or tissue sample. Each profiled biological sample is thus represented by a set of hundreds of thousands of multidimensional cell feature vectors, which incurs a high computational cost to predict each biological sample’s associated phenotype with machine learning models. Such a large set cardinality also limits the interpretability of machine learning models due to the difficulty in tracking how each individual cell influences the ultimate prediction. We propose using Kernel Mean Embedding to encode the cellular landscape of each profiled biological sample. Although our foremost goal is to make a more transparent model, we find that our method achieves comparable or better accuracies than the state-of-the-art gating-free methods through a simple linear classifier. As a result, our model contains few parameters but still performs similarly to deep learning models with millions of parameters. In contrast with deep learning approaches, the linearity and sub-selection step of our model makes it easy to interpret classification results. Analysis further shows that our method admits rich biological interpretability for linking cellular heterogeneity to clinical phenotype. © 2022, CC0.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Subdomain dynamics enable chemical chain reactions in non-ribosomal peptide synthetases

引用

Nature Chemistry 2024年第2期16卷 259-268页

作者： Sun, Xun Alfermann, Jonas Li, Hao Watkins, Maxwell B. Chen, Yi-Tsao Morrell, Thomas E. Mayerthaler, Florian Wang, Chia-Ying Komatsuzaki, Tamiki Chu, Jhih-Wei Ando, Nozomi Mootz, Henning D. Yang, Haw Department of Chemistry Princeton University Princeton NJ United States Institute of Biochemistry University of Münster Münster Germany Department of Chemistry and Chemical Biology Cornell University Ithaca NY United States Institute of Bioinformatics and Systems Biology Institute of Molecular Medicine and Bioengineering Department of Biological Science and Technology Centre for Intelligent Drug Systems and Smart Bio-devices (IDS²B) National Yang Ming Chiao Tung University Hsinchu Taiwan Research Centre of Mathematics for Social Creativity Research Institute for Electronic Science The Institute for Chemical Reaction Design and Discovery (WPI-ICReDD) Hokkaido University Sapporo Japan The Institute of Scientific and Industrial Research Osaka University Ibaraki Japan Department of Integrative Structural and Computational Biology The Scripps Research Institute La Jolla CA United States

Many peptide-derived natural products are produced by non-ribosomal peptide synthetases (NRPSs) in an assembly-line fashion. Each amino acid is coupled to a designated peptidyl carrier protein (PCP) through two distinct reactions catalysed sequentially by the single active site of the adenylation domain (A-domain). Accumulating evidence suggests that large-amplitude structural changes occur in different NRPS states;yet how these molecular machines orchestrate such biochemical sequences has remained elusive. Here, using single-molecule Förster resonance energy transfer, we show that the A-domain of gramicidin S synthetase I adopts structurally extended and functionally obligatory conformations for alternating between adenylation and thioester-formation structures during enzymatic cycles. Complementary biochemical, computational and small-angle X-ray scattering studies reveal interconversion among these three conformations as intrinsic and hierarchical where intra-A-domain organizations propagate to remodel inter-A–PCP didomain configurations during catalysis. The tight kinetic coupling between structural transitions and enzymatic transformations is quantified, and how the gramicidin S synthetase I A-domain utilizes its inherent conformational dynamics to drive directional biosynthesis with a flexibly linked PCP domain is revealed. [Figure not available: see fulltext.]. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Enzyme mechanisms Reaction kinetics and dynamics SAXS Single-molecule biophysics

来源：评论

学校读者我要写书评

暂无评论

Learning Multi-cellular Representations of Single-Cell Transcriptomics Data Enables Characterization of Patient-Level Disease States 29th

Learning Multi-cellular Representations of Single-Cell Tran...

引用

29th International Conference on Research in computational Molecular biology, RECOMB 2025

作者： Liu, Tianyu De Brouwer, Edward Kuo, Tony Diamant, Nathaniel Missarova, Alsu Wang, Hanchen Hao, Minsheng Bravo, Hector Corrada Scalia, Gabriele Regev, Aviv Heimberg, Graham Research and Early Development Genentech South San FranciscoCA94080 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Roche Informatics F. Hoffmann-La Roche Ltd. Mississauga Canada Department of Computer Science Stanford University Palo AltoCA94035 United States

ISBN: (纸本)9783031902512

Single-cell RNA-seq (scRNA-seq) has become a prominent tool for studying human biology and disease. The availability of massive scRNA-seq datasets and advanced machine learning techniques has recently driven the development of single-cell foundation models that provide informative and versatile cell representations based on expression profiles. However, to understand disease states, we need to consider entire tissue ecosystems, simultaneously considering many different interacting cells. Here, we tackle this challenge by generating patient-level representations derived from multi-cellular expression context measured with scRNA-seq of tissues. We develop PaSCient, a novel model that employs a multi-level representation learning paradigm and provides importance scores at the individual cell and gene levels for fine-grained analysis across multiple cell types and gene programs characteristic of a given disease. We apply PaSCient to learn a disease model across a large-scale scRNA-seq atlas of 12.5 million cells from over 2,700 patients. Comprehensive and rigorous benchmarking demonstrates the superiority of PaSCient in disease classification and its multiple downstream applications, including dimensionality reduction, gene/cell type prioritization, and patient subgroup discovery. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Biomolecules

来源：评论

学校读者我要写书评

暂无评论

Scalable On-Chain Storage and Querying for Biomedical Certificates: A Blockchain Solution for Large Data Handling

SSRN

引用

SSRN 2024年

作者： Ni, Eric Knight, Elizabeth Gerstein, Mark Program in Computational Biology and Bioinformatics Yale University 100 College Street New HavenCT06511 United States Department of Molecular Biophysics & Biochemistry Yale University 266 Whitney Avenue New HavenCT06511 United States Yale School of Medicine 333 Cedar Street New HavenCT06510 United States

Blockchain technology is gaining increased traction in the biomedical sector due to its ability to improve trust and reduce the risk of fraud and errors in the handling of health data. However, the large volume of biomedical datasets has slowed its adoption due to poor scalability. This challenge is especially relevant for applications that rely on blockchain’s strong immutability by storing data directly on-chain. Here, we demonstrate the potential of blockchain to create a secure and trustless environment for managing large on-chain records. Specifically, we detail an efficient, index-based approach for storing data on the Ethereum blockchain. We show that insertion and retrieval speeds remain nearly constant relative to database size, scaling linearly with the amount of data processed. Additionally, we achieve substantial efficiency gains through low-level assembly optimizations on the Ethereum Virtual Machine, underscoring the limitations of the Solidity compiler. Finally, we illustrate this approach through a practical case study, designing a smart contract for storing and querying training certificates on the Ethereum blockchain. Our solution achieves ~2x faster data insertion, ~500x faster retrieval, ~60% lower gas costs, and 50% lower storage usage compared to baseline methods. It won first place for track 1 of the 2022 iDASH secure genome analysis competition. We also demonstrate that this solution readily adapts to other data types, enabling efficient on-chain storage and retrieval of text, RNA-seq, or biomedical image data. © 2024, The Authors. All rights reserved.

关键词： Chains

来源：评论

学校读者我要写书评

暂无评论

Mass cytometry uncovers a distinct peripheral immune profile and upregulated CD38 expression in patients with hidradenitis suppurativa

引用

Cellular & Molecular Immunology 2023年第8期20卷 972-975页

作者： Peter Dimitrion Iltefat Hamzavi Congcong Yin Jugmohit Toor Kalpana Subedi Namir Khalasawi Angela Miller Richard Huggins Indra Adrianto Jesse Veenstra Gautham Vellaichamy Aakash Hans Steven Daveluy Mohammad Athar Wilson Liao Henry Lim David Ozog Li Zhou Qing-Sheng Mi Center for Cutaneous Biology and Immunology Research Department of DermatologyHenry Ford Health SystemDetroitMI48202USA Immunology Research Program Henry Ford Cancer InstituteHenry Ford Health SystemDetroitMI48202USA Cancer Biology Graduate Program School of MedicineWayne State UniversityDetroitMI48202USA Center for Bioinformatics Department of Public Health SciencesHenry Ford Health SystemDetroitMI48202USA Department of Dermatology Wayne State University School of MedicineDetroitMI48202USA University of Alabama at Birmingham(UAB)Research Center of Excellence in Arsenicals Department of DermatologyUniversity of Alabama at BirminghamBirminghamALUSA Department of Dermatology University of California San FranciscoSan FranciscoCAUSA Department of Biochemistry Microbiologyand ImmunologySchool of MedicineWayne State UniversityDetroitMI48202USA

Hidradenitis suppurativa(HS)is a chronic inflammatory skin disease with a worldwide prevalence of 0.3–4.0%,wherein abscesses and dermal sinus tracts form in intertriginous skin[1].HS remains poorly treated due to a l... 详细信息

关键词： CD38 patients pathogenesis

来源：评论

学校读者我要写书评

暂无评论

PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion

arXiv

引用

arXiv 2024年

作者： Tang, Sophia Zhang, Yinuo Chatterjee, Pranam Department of Biomedical Engineering Duke University United States Management and Technology Program University of Pennsylvania United States Center of Computational Biology Duke-NUS Medical School Singapore Department of Computer Science Duke University United States Department of Biostatistics and Bioinformatics Duke University United States

Peptide therapeutics, a major class of medicines, have achieved remarkable success across diseases such as diabetes and cancer, with landmark examples such as GLP-1 receptor agonists revolutionizing the treatment of type-2 diabetes and obesity. Despite their success, designing peptides that satisfy multiple conflicting objectives, such as target binding affinity, solubility, and membrane permeability, remains a major challenge. Classical drug development and target structure-based design are ineffective for such tasks, as they fail to optimize global functional properties critical for therapeutic efficacy. Existing generative frameworks are largely limited to continuous spaces, unconditioned outputs, or single-objective guidance, making them unsuitable for discrete sequence optimization across multiple properties. To address this, we present PepTune, a multi-objective discrete diffusion model for the simultaneous generation and optimization of therapeutic peptide SMILES. Built on the Masked Discrete Language Model (MDLM) framework, PepTune ensures valid peptide structures with bond-dependent masking schedules and penalty-based objectives. To guide the diffusion process, we propose a Monte Carlo Tree Search (MCTS)-based strategy that balances exploration and exploitation to iteratively refine Pareto-optimal sequences. MCTS integrates classifier-based rewards with search-tree expansion, overcoming gradient estimation challenges and data sparsity. Using PepTune, we generate diverse, chemically modified peptides optimized for multiple therapeutic properties, including target binding affinity, membrane permeability, solubility, hemolysis, and non-fouling for various disease-relevant targets. In total, our results demonstrate that MCTS-guided masked discrete diffusion is a powerful and modular approach for multi-objective sequence design in discrete state spaces. © 2024, CC BY-NC-ND.

关键词： Trees (mathematics)

来源：评论

学校读者我要写书评

暂无评论

Connecting polymer collapse and the onset of jamming

引用

Physical Review E 2024年第3期109卷 034406-034406页

作者： Alex T. Grigas Aliza Fisher Mark D. Shattuck Corey S. O'Hern Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA

Previous studies have shown that the interiors of proteins are densely packed, reaching packing fractions that are as large as those found for static packings of individual amino-acid-shaped particles. How can the interiors of proteins take on such high packing fractions given that amino acids are connected by peptide bonds and many amino acids are hydrophobic with attractive interactions? We investigate this question by comparing the structural and mechanical properties of collapsed attractive disk-shaped bead-spring polymers to those of three reference systems: static packings of repulsive disks, of attractive disks, and of repulsive disk-shaped bead-spring polymers. We show that the attractive systems quenched to temperatures below the glass transition T≪Tg and static packings of both repulsive disks and bead-spring polymers possess similar interior packing fractions. Previous studies have shown that static packings of repulsive disks are isostatic at jamming onset, i.e., the number of interparticle contacts Nc matches the number of degrees of freedom, which strongly influences their mechanical properties. We find that repulsive polymer packings are hypostatic at jamming onset (i.e., with fewer contacts than degrees of freedom) but are effectively isostatic when including stabilizing quartic modes, which give rise to quartic scaling of the potential energy with displacements along these modes. While attractive disk and polymer packings are often considered hyperstatic with excess contacts over the isostatic number, we identify a definition for interparticle contacts for which they can also be considered as effectively isostatic. As a result, we show that the mechanical properties (e.g., scaling of the potential energy with excess contact number and low-frequency contribution to the density of vibrational modes) of weakly attractive disk and polymer packings are similar to those of isostatic repulsive disk and polymer packings. Our results demonstrate that static

关键词： Jamming Polymer folding Protein folding Single polymer chains

来源：评论

学校读者我要写书评

暂无评论

Tracking SARS-CoV-2 Omicron diverse spike gene mutations identifies multiple inter-variant recombination events

引用

Signal Transduction and Targeted Therapy 2022年第5期7卷 1808-1816页

作者： Junxian Ou Wendong Lan Xiaowei Wu Tie Zhao Biyan Duan Peipei Yang Yi Ren Lulu Quan Wei Zhao Donald Seto James Chodosh Zhen Luo Jianguo Wu Qiwei Zhang Guangdong Provincial Key Laboratory of Virology Institute of Medical MicrobiologyJinan University510632 GuangzhouChina BSL-3 Laboratory(Guangdong) Guangdong Provincial Key Laboratory of Tropical Disease ResearchSchool of Public HealthSouthern Medical University510515 GuangzhouChina Bioinformatics and Computational Biology Program School of Systems BiologyGeorge Mason UniversityManassasVA 20110USA Department of Ophthalmology Howe Laboratory Massachusetts Eye and EarHarvard Medical SchoolBostonMA 02114USA Foshan Institute of Medical Microbiology 528315 FoshanChina

The current pandemic of COVID-19 is fueled by more infectious emergent Omicron *** concerns of emergent variants include possible recombinants,as genome recombination is an important evolutionary mechanism for the emergence and re-emergence of human viral *** this study,we identified diverse recombination events between two Omicron major subvariants(BA.1 and BA.2)and other variants of concern(VOCs)and variants of interest(VOIs),suggesting that co-infection and subsequent genome recombination play important roles in the ongoing evolution of *** scanning high-quality completed Omicron spike gene sequences.

关键词： subsequent diverse recombination

来源：评论

学校读者我要写书评

暂无评论

Bayesian multinomial logistic normal models through marginally latent matrix-T processes

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2022年第1期23卷 255-296页

作者： Justin D. Silverman Kimberly Roche Zachary C. Holmes Lawrence A. David Sayan Mukherjee College of Information Science and Technology Department of Statistics and Institute for Computational and Data Science Penn State University University Park PA Program in Computational Biology and Bioinformatics Duke University Durham NC Department of Molecular Genetics and Microbiology Duke University Durham NC Department of Molecular Genetics and Microbiology and Center for Genomic and Computational Biology Duke University Durham NC Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University Durham NC

Bayesian multinomial logistic-normal (MLN) models are popular for the analysis of sequence count data (e.g., microbiome or gene expression data) due to their ability to model multivariate count data with complex covariance structure. However, existing implementations of MLN models are limited to small datasets due to the non-conjugacy of the multinomial and logistic-normal distributions. Motivated by the need to develop efficient inference for Bayesian MLN models, we develop two key ideas. First, we develop the class of Marginally Latent Matrix-T Process (Marginally LTP) models. We demonstrate that many popular MLN models, including those with latent linear, non-linear, and dynamic linear structure are special cases of this class. Second, we develop an efficient inference scheme for Marginally LTP models with specific accelerations for the MLN subclass. Through application to MLN models, we demonstrate that our inference scheme are both highly accurate and often 4-5 orders of magnitude faster than MCMC.

关键词： Bayesian statistics multivariate analysis count data microbiome gene expression

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：