检索结果-内蒙古大学图书馆

Robocop: Multivariate state space model integrating epigenomic accessibility data to elucidate genome-wide chromatin occupancy 24th

学校读者我要写书评

暂无评论

Robocop: Multivariate state space model integrating epigenom...

24th Annual Conference on Research in computational Molecular biology, RECOMB 2020

作者： Mitra, Sneha Zhong, Jianling MacAlpine, David M. Hartemink, Alexander J. Department of Computer Science Duke University DurhamNC27708 United States Program in Computational Biology and Bioinformatics Duke University DurhamNC27708 United States Department of Pharmacology and Cancer Biology Duke University Medical Center DurhamNC27710 United States Center for Genomic and Computational Biology Duke University DurhamNC27708 United States

ISBN: (纸本)9783030452568

Chromatin is the tightly packaged structure of DNA and protein within the nucleus of a cell. The arrangement of different protein complexes along the DNA modulates and is modulated by gene expression. Measuring the binding locations and level of occupancy of different transcription factors (TFs) and nucleosomes is therefore crucial to understanding gene regulation. Antibody-based methods for assaying chromatin occupancy are capable of identifying the binding sites of specific DNA binding factors, but only one factor at a time. On the other hand, epigenomic accessibility data like ATAC-seq, DNase-seq, and MNase-seq provide insight into the chromatin landscape of all factors bound along the genome, but with minimal insight into the identities of those factors. Here, we present RoboCOP, a multivariate state space model that integrates chromatin information from epigenomic accessibility data with nucleotide sequence to compute genome-wide probabilistic scores of nucleosome and TF occupancy, for hundreds of different factors at once. RoboCOP can be applied to any epigenomic dataset that provides quantitative insight into chromatin accessibility in any organism, but here we apply it to MNase-seq data to elucidate the protein-binding landscape of nucleosomes and 150 TFs across the yeast genome. Using available protein-binding datasets from the literature, we show that our model more accurately predicts the binding of these factors genome-wide. © Springer Nature Switzerland AG 2020.

关键词： Hidden Markov models

Continuous Spatiotemporal Transformers

学校读者我要写书评

暂无评论

arXiv 2023年

作者： de Oliveira Fonseca, Antonio Henrique Zappala, Emanuele Caro, Josue Ortega van Dijk, David Interdepartmental Neuroscience Program Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Neuroscience Yale University New HavenCT United States Wu Tsai Institute Yale University New HavenCT United States Yale University New HavenCT United States Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT United States

Modeling spatiotemporal dynamical systems is a fundamental challenge in machine learning. Transformer models have been very successful in NLP and computer vision where they provide interpretable representations of data. However, a limitation of transformers in modeling continuous dynamical systems is that they are fundamentally discrete time and space models and thus have no guarantees regarding continuous sampling. To address this challenge, we present the Continuous Spatiotemporal Transformer (CST), a new transformer architecture that is designed for modeling of continuous systems. This new framework guarantees a continuous and smooth output via optimization in Sobolev space. We benchmark CST against traditional transformers as well as other spatiotemporal dynamics modeling methods and achieve superior performance in a number of tasks on synthetic and real systems, including learning brain dynamics from calcium imaging data. © 2023, CC BY-NC-SA.

关键词： Sobolev spaces

Flow and clogging of capillary droplets

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Cheng, Yuxuan Lonial, Benjamin F. Sista, Shivnag Meer, David J. Hofert, Anisa Weeks, Eric R. Shattuck, Mark D. O'Hern, Corey S. Department of Physics Yale University New HavenCT06520 United States Department of Physics Emory University AtlantaGA30322 United States Benjamin Levich Institute Physics Department The City College of New York New YorkNY10031 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Capillary droplets form due to surface tension when two immiscible fluids are mixed. We describe the motion of gravity-driven capillary droplets flowing through narrow constrictions and obstacle arrays in both simulations and experiments. Our new capillary deformable particle model recapitulates the shape and velocity of single oil droplets in water as they pass through narrow constrictions in microfluidic chambers. Using this experimentally validated model, we simulate the flow and clogging of single capillary droplets in narrow channels and obstacle arrays and find several important results. First, the capillary droplet speed profile is nonmonotonic as the droplet exits the narrow orifice, and we can tune the droplet properties so that the speed overshoots the terminal speed far from the constriction. Second, in obstacle arrays, we find that extremely deformable droplets can wrap around obstacles, which leads to decreased average droplet speed in the continuous flow regime and increased probability for clogging in the regime where permanent clogs form. Third, the wrapping mechanism causes the clogging probability in obstacle arrays to become nonmonotonic with surface tension Γ. At large Γ, the droplets are nearly rigid and the clogging probability is large since the droplets can not squeeze through the gaps between obstacles. With decreasing Γ, the clogging probability decreases as the droplets become more deformable. However, in the small-Γ limit, the clogging probability increases since the droplets are extremely deformable and wrap around the obstacles. The results from these studies are important for developing a predictive understanding of capillary droplet flows through complex and confined geometries. © 2024, CC BY.

关键词： Drop formation

SRAMM: Short read alignment mapping metrics

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Chon, Alvin Huang, Xiaoqiu Bioinformatics and Computational Biology Graduate Program Iowa State University AmesIA50010 United States Department of Computer Science Iowa State University AmesIA50010 United States

Short Read Alignment Mapping Metrics (SRAMM): is an efficient and versatile command line tool providing additional short read mapping metrics, filtering, and graphs. Short read aligners report MAPing Quality (MAPQ), but these methods generally are neither standardized nor well described in literature or software manuals. Additionally, third party mapping quality programs are typically computationally intensive or designed for specific applications. SRAMM efficiently generates multiple different concept-based mapping scores to provide for an informative post alignment examination and filtering process of aligned short reads for various downstream applications. SRAMM is compatible with Python 2.6+ and Python 3.6+ on all operating systems. It works with any short read aligner that generates SAM/BAM/CRAM file outputs and reports 'AS' tags. It is freely available under the MIT license at http://***/achon/sramm. © 2021, CC BY.

关键词： Mapping

Estimating Causal Effects using a Multi-task Deep Ensemble

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Jiang, Ziyang Hou, Zhuoran Liu, Yiling Ren, Yiman Li, Keyu Carlson, David Department of Civil and Environmental Engineering Duke University DurhamNC United States Department of Biostatistics and Bioinformatics Duke University School of Medicine DurhamNC United States Program in Computational Biology and Bioinformatics Duke University School of Medicine DurhamNC United States Department of Economics University of Michigan Ross School of Business Ann ArborMI United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Computer Science Duke University DurhamNC United States

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks. © 2023, CC BY.

关键词： Data handling

Special issue on genome wide association study

学校读者我要写书评

暂无评论

Quantitative biology 2021年第2期9卷 105-106页

作者： Lin Hou Qiongshi Lu Can Yang Hongyu Zhao Center for Statistical Science Department of Industrial EngineeringMOE Key Laboratory of BioinformaticsSchool of Life SciencesTsinghua UniversityBeijing 100084China Department of Biostatistics and Medical Informatics Department of StatisticsCenter for Demography of Health and AgingUniversity of Wisconsin-MadisonWl 53726USA Department of Mathematics the Hong Kong University of Science and TechnologyHong KongChina Department of Biostatistics Yale School of Public HealthNew HavenCT 06510USA Department of Genetics Yale UniversityNew HavenCT 06510USA Program of Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06510USA

Since the first success of genome wide association study(GWAS)was reported in 2005 by Hoh and colleagues on the identification of a major gene for age related macular degeneration,many thousands of published studies have reported reproducible association signals between hundreds of thousands of genetic variants and thousands of traits and diseases in the past 16 *** is a growing number of biobanks with up to one million participants developed across the world to facilitate genetic studies of complex traits through ***,many consortium projects have been initiated to collect diverse types of data that have proved very valuable for integrated analysis of GWAS *** rich data present both great opportunities and challenges to scientists on data management,computation,analysis,integration,and *** special issue of Quantitative biology collects nine papers that cover major progresses,challenges,and future directions for GWAS analysis.

关键词： hundreds GWAS initiated

Plant scent and plant-insect interactions—Review and outlook from a macroevolutionary perspective

学校读者我要写书评

暂无评论

植物分类学报 2023年第3期61卷 465-486页

作者： Orlando Schwery Breanna N.Sipley Mariana P.Braga Yan Yang Roberto Rebollo Pengjuan Zu New Mexico Consortium 4200 W.Jemez RdSuite 301Los AlamosNM 87544USA Department of Biological Sciences University of Idaho875 Perimeter Dr.MS 3051MoscowID 83844USA Department of Biological Sciences Southeastern Louisiana University2400 N.Oak StHammondLA 70402USA Program for Bioinformatics and Computational Biology University of IdahoMoscowID 83844USA Department of Biology Washington University in St.LouisSt.LouisMO 63130USA Department of Ecology Swedish University of Agricultural SciencesUppsalaSweden Helsinki Life Science Institute University of HelsinkiHelsinkiFinland Key Laboratory of Genetics and Germplasm Innovation of Tropical Special Forest Trees and Ornamental Plants Ministry of EducationCollege of ForestryHainan UniversityHaikou 570228P.R.China Department of Environmental Systems Science ETH ZurichSchmelzbergstrasse 9CH-8092 ZurichSwitzerland Department of Environmental Systems Science ETH ZurichSchmelzbergstrasse 9CH-8092 ZurichSwitzerland Swiss Federal Institute of Aquatic Science and Technology(Eawag) Seestrasse 79CH-6047 KastanienbaumSwitzerland

The astonishing diversity of plants and insects and their entangled interactions are cornerstones in terrestrial ***-occurring with species diversity is the diversity of plant secondary metabolites(PSMs).So far,their estimated number is more than 200 000 compounds,which are not directly involved in plant growth and development but play important roles in helping plants handle their environment including the mediation of plant-insect ***,we use plant volatile organic compounds(VOCs),a key olfactory communication channel that mediates plant-insect interactions,as a showcase of *** spite of the cumulative knowledge of the functional,ecological,and microevolutionary roles of VOCs,we still lack a macroevolutionary understanding of how they evolved with plant-insect interactions and contributed to species diversity throughout the long coevolutionary history of plants and *** first review the literature to summarize the current state-of-the-art research on this *** then present various relevant types of phylogenetic methods suitable to answer macroevolutionary questions on plant VOCs and suggest future directions for employing phylogenetic approaches in studying plant VOCs and plant-insect ***,we found that current studies in this field are still very limited in their macroevolutionary ***,with the fast-growing development of metabolome analysis techniques and phylogenetic methods,it is becoming increasingly feasible to integrate the advances of these two *** highlight promising approaches to generate new testable hypotheses and gain a mechanistic understanding of the macroevolutionary roles of chemical communication in plant-insect interactions.

关键词： coevolution

Graph Fourier MMD for Signals on Graphs

学校读者我要写书评

暂无评论

Graph Fourier MMD for Signals on Graphs

International Conference on Sampling Theory and Applications (SampTA)

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

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 the 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.

关键词：

Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways

学校读者我要写书评

暂无评论

Scientific Reports 2025年第1期15卷 1-18页

作者： Taoma, Kittisak Ruengjitchatchawalya, Marasri Kusonmano, Kanthida Termsaithong, Teerasit Sutthibutpong, Thana Liangruksa, Monrudee Laomettachit, Teeraphan Bioinformatics and Systems Biology Program School of Bioresources and Technology King Mongkut’s University of Technology Thonburi Bangkok 10150 Thailand School of Information Technology King Mongkut’s University of Technology Thonburi Bangkok 10140 Thailand Biotechnology Program School of Bioresources and Technology King Mongkut’s University of Technology Thonburi Bangkok 10150 Thailand Systems Biology and Bioinformatics Research Group Pilot Plant Development and Training Institute King Mongkut’s University of Technology Thonburi Bangkok 10150 Thailand Learning Institute King Mongkut’s University of Technology Thonburi Bangkok 10140 Thailand Theoretical and Computational Physics Group Center of Excellence in Theoretical and Computational Science King Mongkut’s University of Technology Thonburi Bangkok 10140 Thailand Department of Physics Faculty of Science King Mongkut’s University of Technology Thonburi Bangkok 10140 Thailand National Nanotechnology Center (NANOTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani 12120 Thailand

Breast cancer is a complex and challenging disease to treat, and despite progress in combating it, drug resistance remains a significant hindrance. Drug combinations have shown promising results in improving therapeutic outcomes, and many machine learning models have been proposed to identify potential drug combinations. Recently, there has been a growing emphasis on enhancing the interpretability of machine learning models to improve our biological understanding of the drug mechanisms underlying the predictions. In this study, we developed a random forest model using simulated protein activities derived from Boolean modeling of breast cancer signaling pathways as input features. The model demonstrates a moderate Pearson’s correlation coefficient of 0.40 between the predicted and experimentally observed synergistic scores, with the area under the curve (AUC) of 0.67. Despite its moderate performance, the model offers insights into the interpretable mechanisms behind its predictions. The model’s input features consist solely of the individual protein activities simulated in response to drug treatments. Therefore, the framework allows for the analysis of each protein’s contribution to the synergy level of each drug pair, enabling a direct interpretation of the drugs’ actions on the signaling networks of breast cancer. We demonstrated the interpretability of our approach by identifying proteins responsible for drug resistance and sensitivity in specific cell lines. For example, the analysis revealed that the combination of MEK and STAT3 inhibitors exhibits only a moderate synergistic effect on MDA-MB-468 due to the negative contributions of mTORC1 and NF-κB that diminish the efficacy of the drug pair. The model further predicted that hyperactive PTEN would sensitize the cells to the drug pair. Our framework enhances the understanding of drug mechanisms at the level of the signaling pathways, potentially leading to more effective treatment designs. © The Author(s) 2025.

关键词： Breast cancer Drug combinations Drug resistance Machine learning Signaling pathways