检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Grigas, Alex T. Fisher, Aliza Shattuck, Mark D. O’Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department The City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

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 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 contacts Nc matches the number of degrees of freedom, which strongly influences their mechanical properties. We find that repulsive polymers are hypostatic at jamming onset, but effectively isostatic when including quartic modes. While attractive disk and polymer packings are hyperstatic, 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 packings generated via attractive collapse or compression of repulsive particles possess similar structural and mechanical properties. © 2023, CC BY.

关键词： Amino acids

来源：评论

学校读者我要写书评

暂无评论

Scalable privacy-preserving cancer type prediction with homomorphic encryption

arXiv

引用

arXiv 2022年

作者： Sarkar, Esha Chielle, Eduardo Gursoy, Gamze Chen, Leo Gerstein, Mark Maniatakos, Michail Tandon School of Engineering New York University Brooklyn United States Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Program in Computational Biology and Bioinformatics Yale University New Haven United States Department of Computer Science Yale University New Haven United States

Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer/tumor classification from high-dimensional genetic mutation data can be useful for cancer diagnosis and treatment, if the distinguishable patterns between cancer/tumor types are identified. At the same time, analysis of high-dimensional data is computationally expensive and is often outsourced to cloud services. Privacy concerns in outsourced ML, especially in the field of genetics, motivate the use of encrypted computation, like Homomorphic Encryption (HE). But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer detection using a real-world dataset consisting of more than 2 million genetic information for several cancer types. Since the data is inherently high-dimensional, we explore smaller ML models for cancer prediction to enable fast inference in the privacy preserving domain. We develop a solution for privacy preserving cancer inference which first leverages the domain knowledge on somatic mutations to efficiently encode genetic mutations and then uses statistical tests for feature selection. Our logistic regression model, built using our novel encoding scheme, achieves 0.98 micro-average area under curve with 13% higher test accuracy than similar studies. We exhaustively test our model’s predictive capabilities by analyzing the genes used by the model. Furthermore, we propose a fast matrix multiplication algorithm that can efficiently handle high-dimensional data. Experimental results show that, even with 40,000 features, our proposed matrix multiplication algorithm can speed up concurrent inference of multiple individuals by approximately 10x and inference of a single individual by approximately 550x, in comparison to standard matrix multiplication. © 2022, CC BY.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Doubly-stochastic normalization of the Gaussian kernel is robust to heteroskedastic noise

arXiv

引用

arXiv 2020年

作者： Landa, Boris Coifman, Ronald R. Kluger, Yuval Program in Applied Mathematics Yale University Interdepartmental Program in Computational Biology and Bioinformatics Yale University Department of Pathology Yale University School of Medicine

A fundamental step in many data-analysis techniques is the construction of an affinity matrix describing similarities between data points. When the data points reside in Euclidean space, a widespread approach is to from an affinity matrix by the Gaussian kernel with pairwise distances, and to follow with a certain normalization (e.g. the row-stochastic normalization or its symmetric variant). We demonstrate that the doubly-stochastic normalization of the Gaussian kernel with zero main diagonal (i.e. no self loops) is robust to heteroskedastic noise. That is, the doubly-stochastic normalization is advantageous in that it automatically accounts for observations with different noise variances. Specifically, we prove that in a suitable high-dimensional setting where heteroskedastic noise does not concentrate too much in any particular direction in space, the resulting (doubly-stochastic) noisy affinity matrix converges to its clean counterpart with rate m−1/2, where m is the ambient dimension. We demonstrate this result numerically, and show that in contrast, the popular row-stochastic and symmetric normalizations behave unfavourably under heteroskedastic noise. Furthermore, we provide a prototypical example of simulated single-cell RNA sequence data with strong intrinsic heteroskedasticity, where the advantage of the doubly-stochastic normalization for exploratory analysis is evident. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

CAFA-evaluator: A Python Tool for Benchmarking Ontological Classification Methods

arXiv

引用

arXiv 2023年

作者： Piovesan, Damiano Zago, Davide Joshi, Parnal Clara De Paolis Kaluza, M. Mehdiabadi, Mahta Ramola, Rashika Monzon, Alexander Miguel Reade, Walter Friedberg, Iddo Radivojac, Predrag Tosatto, Silvio C.E. Department of Biomedical Sciences University of Padova Padova35121 Italy Program in Bioinformatics and Computational Biology Iowa State University AmesIA50011 United States Department of Veterinary Microbiology and Preventive Medicine Iowa State University AmesIA50011 United States Khoury College of Computer Sciences Northeastern University BostonMA02115 United States Department of Information Engineering University of Padova Padova35121 Italy Kaggle San FranciscoCA United States

We present CAFA-evaluator, a powerful Python program designed to evaluate the performance of prediction methods on targets with hierarchical concept dependencies. It generalizes multi-label evaluation to modern ontologies where the prediction targets are drawn from a directed acyclic graph and achieves high efficiency by leveraging matrix computation and topological sorting. The program requirements include a small number of standard Python libraries, making CAFA-evaluator easy to maintain. The code replicates the Critical Assessment of protein Function Annotation (CAFA) benchmarking, which evaluates predictions of the consistent subgraphs in Gene Ontology. Owing to its reliability and accuracy, the organizers have selected CAFA-evaluator as the official CAFA evaluation software. © 2023, CC BY.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

ReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteins

arXiv

引用

arXiv 2022年

作者： Castro, Egbert Godavarthi, Abhinav Rubinfien, Julian Givechian, Kevin Bhaskar, Dhananjay Krishnaswamy, Smita Computational Biology and Bioinformatics Program Yale University New HavenCT06511 United States Department of Applied Mathematics Yale University New HavenCT06511 United States Department of Genetics Yale University New HavenCT06511 United States Department of Computer Science Yale University New HavenCT06511 United States

The development of powerful natural language models have increased the ability to learn meaningful representations of protein sequences. In addition, advances in high-throughput mutagenesis, directed evolution, and next-generation sequencing have allowed for the accumulation of large amounts of labeled fitness data. Leveraging these two trends, we introduce Regularized Latent Space Optimization (ReLSO), a deep transformer-based autoencoder which features a highly structured latent space that is trained to jointly generate sequences as well as predict fitness. Through regularized prediction heads, ReLSO introduces a powerful protein sequence encoder and novel approach for efficient fitness landscape traversal. Using ReLSO, we explicitly model the sequence-function landscape of large labeled datasets and generate new molecules by optimizing within the latent space using gradient-based methods. We evaluate this approach on several publicly-available protein datasets, including variant sets of anti-ranibizumab and GFP. We observe a greater sequence optimization efficiency (increase in fitness per optimization step) by ReLSO compared to other approaches, where ReLSO more robustly generates high-fitness sequences. Furthermore, the attention-based relationships learned by the jointly-trained ReLSO models provides a potential avenue towards sequence-level fitness attribution information. © 2022, CC BY-NC-ND.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

Virtual Flavonoids Screening Identifies Potent Compounds Against Enterovirus A71 and Coxsackievirus A16

SSRN

引用

SSRN 2023年

作者： Sripattarphan, Amita Sanachai, Kamonpan Chavasiri, Warinthorn Wolschann, Peter Langer, Thierry Boonyasuppayakorn, Siwaporn Rungrotmongkol, Thanyada Center of Excellence in Structural and Computational Biology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Biochemistry Faculty of Science Khon Kaen University Khon Kaen40002 Thailand Center of Excellence in Natural Products Chemistry Department of Chemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Pharmaceutical Chemistry Faculty of Life Sciences University of Vienna Althanstraße 14 ViennaA-1090 Austria Institute of Theoretical Chemistry University of Vienna Vienna1090 Austria Center of Excellence in Applied Medical Virology Department of Microbiology Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok10330 Thailand

Worldwide outbreaks of hand, foot, and mouth disease (HFMD) are caused by enterovirus A71 (EV-A71) and coxsackievirus A16 (CV-A16). Since no anti-HFMD drugs are currently available, it is interesting to study potential viral inhibitors. Rupintrivir is a rhinovirus 3C protease (3Cpro) inhibitor with apparent inhibitory activity against HFMD. This study constructs pharmacophore models of the EV-A71 and CV-A16 3Cpro complexed with rupintrivir using molecular dynamics (MD) simulations. Both models had similar pharmacophore features, including hydrogen bond donors and acceptors and hydrophobic interactions. These pharmacophore models were then used as a template to screen 39 flavonoid compounds as potential novel inhibitors. Diosmin, epigallocatechin gallate (EGCG), and RTH-011 showed high binding affinities for EV-A71 and CV-A16 3Cpro. They formed hydrogen bonds with important surrounding residues in both proteins, including H40, L127, T142, A144, T145, H161, I162, G163, and G164. In addition, their effective concentrations against rhabdomyosarcoma (RD) cell infection by EV-A71 and CV-A16 were determined. EGCG had the highest half maximal effective concentration (EC50) of 12.86 ± 1.30 µM for EV-A71 and 15.54 ± 1.50 µM for CV-A16, while diosmin had EC50 of 21.01±1.57 µM for EV-A71 and 30.68 ± 3.25 µM for CV-A16. Both compounds were non-toxic in RD cells, with 50% cytotoxic concentrations of >100 µM for EGCG and >500 µM for diosmin. In addition, MD simulation analysis showed that EGCG had a higher binding affinity than diosmin, supported by its significantly lower solvated binding free energies and greater numbers of contact atoms and key binding residues. Moreover, previous studies reported EGCG’s inhibitory effect on other viruses, such as severe acute respiratory syndrome coronavirus 2. Therefore, our findings suggest that EGCG can effectively inhibit the EV-A71 and CV-A16 3Cpro. © 2023, The Authors. All rights reserved.

关键词： Molecular dynamics

来源：评论

学校读者我要写书评

暂无评论

Characterizing Emergency Department Care for Patients With Histories of Incarceration

引用

JACEP Open 2025年第1期6卷 100022-100022页

作者： Huang, Thomas Socrates, Vimig Ovchinnikova, Polina Faustino, Isaac Kumar, Anusha Safranek, Conrad Chi, Ling Wang, Emily A. Puglisi, Lisa Wong, Ambrose H. Wang, Karen H. Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Department 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 SEICHE Center for Health and Justice Yale School of Medicine New Haven CT United States Equity Research and Innovation Center Yale School of Medicine Yale University New Haven CT United States

Objectives: Patients with a history of incarceration experience bias from health care team members, barriers to privacy, and a multitude of health care disparities. We aimed to assess care processes delivered in emergency departments (EDs) for people with histories of incarceration. Methods: We utilized a fine-tuned large language model to identify patient incarceration status from 480,374 notes from the ED setting. We compared socio-demographic characteristics, comorbidities, and care processes, including disposition, restraint use, and sedation, between individuals with and without a history of incarceration. We then conducted multivariable logistic regression to assess the independent correlation of incarceration history and management in the ED while adjusting for demographic characteristics, health behaviors, presentation, and past medical history. Results: We found 1734 unique patient encounters with a history of incarceration from a total of 177,987 encounters. Patients with history of incarceration were more likely to be male, Black, Hispanic, or other race/ethnicity, currently unemployed or disabled, and had smoking and substance use histories, compared with those without. This cohort demonstrated higher odds of elopement (OR: 3.59 [95% CI: 2.41–5.12]), leaving against medical advice (OR: 2.39 [95% CI: 1.46–3.67]), and being subjected to sedation (OR: 3.89 [95% CI: 3.19–4.70]) and restraint use (OR: 3.76 [95% CI: 3.06–4.57]). After adjusting for covariates, the association between incarceration and elopement remained significant (adjusted odds ratio: 1.65 [95% CI: 1.08–2.43]), while associations with other dispositions, restraint use, and sedation did not persist. Conclusion: This study identified differences in patient characteristics and care processes in the ED for patients with histories of incarceration and demonstrated the potential of using natural language processing in measuring care processes in populations that are largely hidden, but highly prev

关键词： artificial intelligence emergency medicine incarceration large language models quality of health care

来源：评论

学校读者我要写书评

暂无评论

Core packing of well-defined x-ray and NMR structures is the same

arXiv

引用

arXiv 2022年

作者： Grigas, Alex T. Liu, Zhuoyi Regan, Lynne O’Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Centre for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh United Kingdom Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Numerous studies have investigated the differences and similarities between protein structures determined by solution NMR spectroscopy and those determined by x-ray crystallography. A fundamental question is whether any observed differences are due to differing methodologies, or to differences in the behavior of proteins in solution versus in the crystalline state. Here, we compare the properties of the hydrophobic cores of high-resolution protein crystal structures and those in NMR structures, determined using increasing numbers and types of restraints. Prior studies have reported that many NMR structures have denser cores compared to those of high-resolution x-ray crystal structures. Our current work investigates this result in more detail, and finds that these NMR structures tend to violate basic features of protein stereochemistry, such as small non-bonded atomic overlaps and few Ramachandran and side chain dihedral angle outliers. We find that NMR structures solved with more restraints, and which do not significantly violate stereochemistry, have hydrophobic cores that have a similar size and packing fraction as their counterparts determined by x-ray crystallography at high-resolution. These results lead us to conclude that, at least regarding the core packing properties, high-quality structures determined by NMR and x-ray crystallography are the same, and the differences reported earlier are most likely a consequence of methodology, rather than fundamental differences between the protein in the two different environments. Significance: Dense packing of hydrophobic residues is key to protein structure and stability. Previously, it has been noted that structures solved by NMR spectroscopy have denser cores than x-ray crystal structures. Here, we calculate the core size and packing fraction of NMR structures with experimental restraints in the PDB. Their cores are typically smaller, but denser. However, NMR structures with accurate stereochemistry possess core pa

关键词： X ray crystallography

来源：评论

学校读者我要写书评

暂无评论

Identifying topologically associating domains using differential kernels

arXiv

引用

arXiv 2023年

作者： Maisuradze, Luka King, Megan C. Surovtsev, Ivan V. Mochrie, Simon G.J. Shattuck, Mark D. O’Hern, Corey S. Department of Molecular Biophysics and Biochemistry Yale University New HavenCT United States Department of Cell Biology Yale School of Medicine New HavenCT United States Department of Physics Yale University New HavenCT United States Benjamin Levich Institute Physics Department The City College of New York New YorkNY United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT United States

Chromatin is a polymer complex of DNA and proteins that regulates gene expression. The three-dimensional (3D) structure and organization of chromatin controls DNA transcription and replication. High-throughput chromatin conformation capture techniques generate Hi-C maps that can provide insight into the 3D structure of chromatin. Hi-C maps can be represented as a symmetric matrix Aij, where each element represents the average contact probability or number of contacts between chromatin loci i and j. Previous studies have detected topologically associating domains (TADs), or self-interacting regions in Aij within which the contact probability is greater than that outside the region. Many algorithms have been developed to identify TADs within Hi-C maps. However, most TAD identification algorithms are unable to identify nested or overlapping TADs and for a given Hi-C map there is significant variation in the location and number of TADs identified by different methods. We develop a novel method to identify TADs, KerTAD, using a kernel-based technique from computer vision and image processing that is able to accurately identify nested and overlapping TADs. We benchmark this method against state-of-the-art TAD identification methods on both synthetic and experimental data sets. We find that the new method consistently has higher true positive rates (TPR) and lower false discovery rates (FDR) than all tested methods for both synthetic and manually annotated experimental Hi-C maps. The TPR for KerTAD is also largely insensitive to increasing noise and sparsity, in contrast to the other methods. We also find that KerTAD is consistent in the number and size of TADs identified across replicate experimental Hi-C maps for several organisms. Thus, KerTAD will improve automated TAD identification and enable researchers to better correlate changes in TADs to biological phenomena, such as enhancer-promoter interactions and disease states. © 2023, CC BY.

关键词： Topology

来源：评论

学校读者我要写书评

暂无评论

Graph Fourier MMD for Signals on Graphs

arXiv

引用

arXiv 2023年

作者： Leone, Samuel Venkat, Aarthi Huguet, Guillaume Tong, Alexander Wolf, Guy Krishnaswamy, Smita Applied Math Program New HavenCT United States Computational Biology & Bioinformatics Program Yale University New HavenCT United States Dept. of Math. & Stat Université de Montréal Mila - Quebec AI Institute MontrealQC Canada Dept. of Comp. Sci. & Oper. Res. Université de Montréal Mila - Quebec AI Institute MontrealQC Canada Department of Computer Science Department of Genetics Yale University New HavenCT United States

While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little attention has been given to computing such distances for distributions on graphs. However, there has been a marked increase in data that either lies on graph (such as protein interaction networks) or can be modeled as a graph (single cell data), particularly in the biomedical sciences. Thus, it becomes important to find ways to compare signals defined on such graphs. Here, we propose Graph Fourier MMD (GFMMD), a novel distance between distributions and signals on graphs. GFMMD is defined via an optimal witness function that is both smooth on the graph and maximizes difference in expectation between the pair of distributions on the graph. We find an analytical solution to this optimization problem as well as an embedding of distributions that results from this method. We also prove several properties of this method including scale invariance and applicability to disconnected graphs. We showcase it on graph benchmark datasets as well on single cell RNA-sequencing data analysis. In the latter, we use the GFMMD-based gene embeddings to find meaningful gene clusters. We also propose a novel type of score for gene selection called gene localization score which helps select genes for cellular state space characterization. Copyright © 2023, The Authors. All rights reserved.

关键词： Genes

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：