检索结果-内蒙古大学图书馆

An unsupervised transfer learning algorithm for sleep monitoring

学校读者我要写书评

暂无评论

arXiv 2019年

作者： She, Xichen Zhai, Yaya Henao, Ricardo Woods, Christopher W. Ginsburg, Geoffrey S. Song, Peter X.K. Hero, Alfred O. Department of Biostatistics University of Michigan Ann ArborMI48109 United States Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI48109 United States Center for Applied Genomics and Precision Medicine Duke University DurhamNC27708 United States Departments of Electrical Engineering and Computer Science Biomedical Engineering and Statistics University of Michigan Ann ArborMI48109 United States

Objective: To develop multisensor-wearable-device sleep monitoring algorithms that are robust to health disruptions affecting sleep patterns. Methods: We develop an unsupervised transfer learning algorithm based on a multivariate hidden Markov model and Fisher's linear discriminant analysis, adaptively adjusting to sleep pattern shift by training on dynamics of sleep/wake states. The proposed algorithm operates, without requiring a priori information about true sleep/wake states, by establishing an initial training set with hidden Markov model and leveraging a taper window mechanism to learn the sleep pattern in an incremental fashion. Our domain-adaptation algorithm is applied to a dataset collected in a human viral challenge study to identify sleep/wake periods of both uninfected and infected participants. Results: The algorithm successfully detects sleep/wake sessions in subjects whose sleep patterns are disrupted by respiratory infection (H3N2 flu virus). Presymptomatic features based on the detected periods are found to be strongly predictive of both infection status (AUC = 0.844) and infection onset time (AUC = 0.885), indicating the effectiveness and usefulness of the algorithm. Conclusion: Our method can effectively detect sleep/wake states in the presence of sleep pattern shift. Significance: Utilizing integrated multisensor signal processing and adaptive training schemes, our algorithm is able to capture key sleep patterns in ambulatory monitoring, leading to better automated sleep assessment and prediction. Copyright © 2019, The Authors. All rights reserved.

关键词： Learning algorithms

Coprime networks of the composite numbers: pseudo-randomness and synchronizability

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Miraj, Md Rahil Ghosh, Dibakar Hens, Chittaranjan Theoretical Statistics and Mathematics Unit Indian Statistical Institute 8th Mile Mysore Rd RVCE Post Karnataka Bengaluru560059 India Physics and Applied Mathematics Unit Indian Statistical Institute 203 B.T. Road West Bengal Kolkata700108 India Center for Computational Natural Science and Bioinformatics International Institute of Informational Technology Gachibowli Telengana Hyderabad500032 India

In this paper, we propose a network whose nodes are labeled by the composite numbers and two nodes are connected by an undirected link if they are relatively prime to each other. As the size of the network increases, the network will be connected whenever the largest possible node index n ≥ 49. To investigate how the nodes are connected, we analytically describe that the link density saturates to 6/π2, whereas the average degree increases linearly with slope 6/π2 with the size of the network. To investigate how the neighbors of the nodes are connected to each other, we find the shortest path length will be at most 3 for 49 ≤ n ≤ 288 and it is at most 2 for n ≥ 289. We also derive an analytic expression for the local clustering coefficients of the nodes, which quantifies how close the neighbors of a node to form a triangle. We also provide an expression for the number of r-length labeled cycles, which indicates the existence of a cycle of length at most O(log n). Finally, we show that this graph sequence is actually a sequence of weakly pseudo-random graphs. We numerically verify our observed analytical results. As a possible application, we have observed less synchronizability (the ratio of the largest and smallest positive eigenvalue of the Laplacian matrix is high) as compared to Erdős–Rényi random network and Barabási-Albert network. This unusual observation is consistent with the prolonged transient behaviors of ecological and predator-prey networks which can easily avoid the global synchronization. © 2024, CC BY.

关键词： Eigenvalues and eigenfunctions

Impact of type 2 diabetes on adenoma detection in screening colonoscopies performed in disparate populations

学校读者我要写书评

暂无评论

World Journal of Clinical Cases 2021年第11期9卷 2433-2445页

作者： Dimitri F Joseph Ellen Li Samuel L Stanley III Yi-Cong Zhu Xiao-Ning Li Jie Yang Lorenzo F Ottaviano Juan Carlos Bucobo Jonathan M Buscaglia Joshua D Miller Rajesh Veluvolu Michele Follen Evan B Grossman Department of Medicine Renaissance School of Medicine at Stony Brook UniversityStony BrookNY 11794-8173United States Department of Medicine Division of GastroenterologyStony Brook UniversityStony BrookNY 11794-8173United States Department of Applied Mathematics and Statistics Stony Brook UniversityStony BrookNY 11794-3600United States Department of Biostatistics and Bioinformatics Shared Resource Stony Brook Cancer CenterStony Brook UniversityStony BrookNY 11794-3600United States Department of Family Populationand Preventative MedicineRenaissance School of Medicine at Stony Brook UniversityStony BrookNY 11794-8461United States Department of Medicine NYC Health and Hospitals/Kings CountyBrooklynNY 11203United States Department of Medicine SUNY Downstate Medical CenterBrooklynNY 11203United States Department of Obstetrics and Gynecology NYC Health and Hospitals/Kings CountyBrooklynNY 11203United States

BACKGROUND The Black/African Ancestry(AA)population has a higher prevalence of type 2 diabetes mellitus(T2DM)and a higher incidence and mortality rate for colorectal cancer(CRC)than all other races in the United States.T2DM has been shown to increase adenoma risk in predominantly white/European ancestry(EA)populations,but the effect of T2DM on adenoma risk in Black/AA individuals is less *** hypothesize that T2DM has a significant effect on adenoma risk in a predominantly Black/AA *** To investigate the effect of T2DM and race on the adenoma detection rate(ADR)in screening colonoscopies in two disparate *** A retrospective cohort study was conducted on ADR during index screening colonoscopies(age 45-75)performed at an urban public hospital serving a predominantly Black/AA population(92%)(2017-2018,n=1606).Clinical metadata collected included basic demographics,insurance,body mass index(BMI),family history of CRC,smoking,diabetes diagnosis,and aspirin *** dataset was combined with a recently reported parallel retrospective cohort data set collected at a suburban university hospital serving a predominantly White/EA population(87%)(2012-2015,n=2882).RESULTS The ADR was higher in T2DM patients than in patients without T2DM or prediabetes(35.2%vs 27.9%,P=0.0166,n=981)at the urban public *** analysis of the combined datasets showed that T2DM[odds ratio(OR)=1.29,95%confidence interval(CI):1.08-1.55,P=0.0049],smoking(current vs never OR=1.47,95%CI:1.18-1.82,current vs past OR=1.32,95%CI:1.02-1.70,P=0.0026),older age(OR=1.05 per year,95%CI:1.04-1.06,P<0.0001),higher BMI(OR=1.02 per unit,95%CI:1.01-1.03,P=0.0003),and male sex(OR=1.87,95%CI:1.62-2.15,P<0.0001)were associated with increased ADR in the combined datasets,but race,aspirin use and insurance were *** T2DM,but not race,is significantly associated with increased ADR on index screening colonoscopy while controlling for other factors.

关键词： Adenoma Diabetes mellitus,type 2 African continental ancestry group European continental ancestry group Colonoscopy Multivariate analysis

Impact of preoperative antibiotics and other variables on integrated microbiome-host transcriptomic data generated from colorectal cancer resections

学校读者我要写书评

暂无评论

World Journal of Gastroenterology 2021年第14期27卷 1465-1482页

作者： Sarah A Malik Chencan Zhu Jinyu Li Joseph F LaComb Paula I Denoya Igor Kravets Joshua D Miller Jie Yang Melissa Kramer W Richard McCombie Charles E Robertson Daniel N Frank Ellen Li Department of Medicine Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794United States Department of Applied Mathematics and Statistics Stony Brook UniversityStony BrookNY 11794United States Stony Brook Cancer Center Biostatistics and Bioinformatics Shared Resource Stony Brook UniversityStony BrookNY 11794United States Department of Pathology Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794United States Department of Surgery Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794United States Department of Family Population and Preventive MedicineStony Brook UniversityStony BrookNY 11794United States Cold Spring Harbor Laboratory Cancer Center Sequencing Technologies and Analysis Shared Resource Cold Spring HarborNY 11724United States Department of Medicine University of Colorado Anschutz Medical CampusAuroraCO 80045United States

BACKGROUND Integrative multi-omic approaches have been increasingly applied to discovery and functional studies of complex human ***-term preoperative antibiotics have been adopted to reduce site infections in colorectal cancer(CRC)*** hypothesize that the antibiotics will impact analysis of multi-omic datasets generated from resection samples to investigate biological CRC risk *** To assess the impact of preoperative antibiotics and other variables on integrated microbiome and human transcriptomic data generated from archived CRC resection *** Genomic DNA(gDNA)and RNA were extracted from prospectively collected 51 pairs of frozen sporadic CRC tumor and adjacent non-tumor mucosal samples from 50 CRC patients archived at a single medical center from *** 16S rRNA gene sequencing(V3V4 region,paired end,300 bp)and confirmatory quantitative polymerase chain reaction(qPCR)assays were conducted on *** sequencing(IPE,125 bp)was performed on parallel tumor and non-tumor RNA samples with RNA Integrity Numbers scores≥*** PERMANOVA detected significant effects of tumor vs nontumor histology(P=0.002)and antibiotics(P=0.001)on microbialβ-diversity,but CRC tumor location(left vs right),diabetes mellitus vs not diabetic and Black/African Ancestry(AA)vs not Black/AA,did not reach *** mixed models detected significant tumor vs nontumor histology*antibiotics interaction terms for 14 genus level *** confirmed increased Fusobacterium abundance in tumor vs nontumor groups,and detected significantly reduced bacterial load in the(+)antibiotics *** coordinate analysis of the transcriptomic data showed a clear separation between tumor and nontumor *** expressed genes obtained from separate analyses of tumor and nontumor samples,are presented for the antibiotics,CRC location,diabetes and Black/AA race *** Recent adoption of additional preoperative antibiotics as sta

关键词： Colorectal cancer Antibiotics African Continental Ancestry Group Diabetes mellitus 16S rRNA gene RNA-sequencing

Maximum pairwise-rank-likelihood-based inference for the semiparametric transformation model

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Yu, Tao Li, Pengfei Chen, Baojiang Yuan, Ao Qin, Jing Department of Statistics & Applied Probability National University of Singapore Singapore117546 Singapore Department of Statistics and Actuarial Science University of Waterloo WaterlooONN2L 3G1 Canada Department of Biostatistics and Data Science University of Texas Health Science Center at Houston School of Public Health in Austin AustinTX78701 United States Department of Biostatistics Bioinformatics and Biomathematics Georgetown University WashingtonDC20057 United States National Institute of Allergy and Infectious Diseases National Institutes of Health MD20892 United States

MSC Codes 62J02, 62J05, 62J86In this paper, we study the linear transformation model in the most general setup. This model includes many important and popular models in statistics and econometrics as special cases. Although it has been studied for many years, the methods in the literature are based on kernel-smoothing techniques or make use of only the ranks of the responses in the estimation of the parametric components. The former approach needs a tuning parameter, which is not easily optimally specified in practice;and the latter is computationally expensive and may not make full use of the information in the data. In this paper, we propose two methods: a pairwise rank likelihood method and a score-function-based method based on this pairwise rank likelihood. We also explore the theoretical properties of the proposed estimators. Via extensive numerical studies, we demonstrate that our methods are appealing in that the estimators are not only robust to the distribution of the random errors but also lead to mean square errors that are in many cases comparable to or smaller than those of existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Linear transformations

Spectral Top-Down Recovery of Latent Tree Models

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Aizenbud, Yariv Jaffe, Ariel Wang, Meng Hu, Amber Amsel, Noah Nadler, Boaz Chang, Joseph T. Kluger, Yuval Program in Applied Mathematics Yale University New HavenCT06511 United States Department of Computer Science Weizmann Institute of Science Rehovot76100 Israel Department of Statistics Yale University New HavenCT06520 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Department of Pathology Yale University New HavenCT06511 United States

Modeling the distribution of high dimensional data by a latent tree graphical model is a prevalent approach in multiple scientific domains. A common task is to infer the underlying tree structure, given only observations of its terminal nodes. Many algorithms for tree recovery are computationally intensive, which limits their applicability to trees of moderate size. For large trees, a common approach, termed divide-and-conquer, is to recover the tree structure in two steps. First, recover the structure separately of multiple, possibly random subsets of the terminal nodes. Second, merge the resulting subtrees to form a full tree. Here, we develop Spectral Top-Down Recovery (STDR), a deterministic divide-and-conquer approach to infer large latent tree models. Unlike previous methods, STDR partitions the terminal nodes in a non random way, based on the Fiedler vector of a suitable Laplacian matrix related to the observed nodes. We prove that under certain conditions, this partitioning is consistent with the tree structure. This, in turn, leads to a significantly simpler merging procedure of the small subtrees. We prove that STDR is statistically consistent and bound the number of samples required to accurately recover the tree with high probability. Using simulated data from several common tree models in phylogenetics, we demonstrate that STDR has a significant advantage in terms of runtime, with improved or similar accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Recovery

Proceedings of the EuBIC-MS 2020 Developers’ Meeting

学校读者我要写书评

暂无评论

EuPA Open Proteomics 2020年 24卷 1-6页

作者： Christopher Ashwood Wout Bittremieux Eric W. Deutsch Nadezhda T. Doncheva Viktoria Dorfer Ralf Gabriels Vladimir Gorshkov Surya Gupta Andrew R. Jones Lukas Käll Dominik Kopczynski Lydie Lane Ludwig Lautenbacher Marc Legeay Marie Locard-Paulet Bart Mesuere Yasset Perez-Riverol Eugen Netz Sander Willems European Bioinformatics Community for Mass Spectrometry (info@***) Denmark Department of Cellular and Integrative Physiology University of Nebraska Medical Center Omaha USA Department of Computer Science University of Antwerp Antwerp Belgium Institute for Systems Biology Seattle WA 98109 USA Novo Nordisk Foundation Center for Protein Research University of Copenhagen Denmark Bioinformatics Research Group University of Applied Sciences Upper Austria Hagenberg Austria VIB-UGent Center for Medical Biotechnology VIB Ghent Belgium Department of Biochemistry and Molecular Biology University of Southern Denmark Denmark Institute of Systems Molecular and Integrative Biology University of Liverpool UK Science for Life Laboratory KTH - Royal Institute of Technology Solna Stockholm Sweden Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V. Dortmund Germany SIB-Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine Faculty of Medicine Geneva University Geneva Switzerland Technical University of Munich Chair for Proteomics and Bioanalytics Freising Germany European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Wellcome Trust Genome Campus Hinxton Cambridge CB10 1SD UK Applied Bioinformatics Dept. of Computer Science University of Tübingen Tübingen Germany Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA 92093 USA Center for non-coding RNA in Technology and Health Department of Veterinary and Animal Sciences University of Copenhagen Denmark Department of Biomolecular Medicine Ghent University Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Biomolecular Interactions Max Planck Institute for Developmental Biology Tübingen Germany

The 2020 European bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers’ meeting was held from January 13 th to January 17 th 2020 in Nyborg, Denmark. Among the participants were scientists as well as developers working in the field of computational mass spectrometry (MS) and proteomics. The 4-day program was split between introductory keynote lectures and parallel hackathon sessions. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts, and to actively contribute to highly relevant research projects. We successfully produced several new tools that will be useful to the proteomics community by improving data analysis as well as facilitating future research. All keynote recordings are available on https://***/10.5281/zenodo.3890181 .

关键词： Computational mass spectrometry Mass spectrometry Proteomics bioinformatics Spectrum clustering Phosphoproteomics XIC extraction Proteomics graph networks Predicted spectra Metaproteomics Functional annotations Benchmark development

A FLOW ARTIST FOR HIGH-DIMENSIONAL CELLULAR DATA

学校读者我要写书评

暂无评论

arXiv 2023年

作者： MacDonald, Kincaid Bhaskar, Dhananjay Thampakkul, Guy Nguyen, Nhi Zhang, Joia Perlmutter, Michael Adelstein, Ian Krishnaswamy, Smita Department of Mathematics Yale University United States Department of Genetics Yale School of Medicine United States Department of Computer Science Yale University United States Department of Mathematics Pomona College United States Applied Mathematics Program Yale University United States Department of Statistics University of Washington United States Department of Mathematics Boise State University United States Computational Biology and Bioinformatics Program Yale University United States

We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured, including in high-throughput biology such as single-cell transcriptomics. Existing embedding techniques either do not utilize velocity information or embed the coordinates and velocities independently, i.e., they either impose velocities on top of an existing point embedding or embed points within a prescribed vector field. Here we present FlowArtist, a neural network that embeds points while jointly learning a vector field around the points. The combination allows FlowArtist to better separate and visualize velocity-informed structures. Our results, on toy datasets and single-cell RNA velocity data, illustrate the value of utilizing coordinate and velocity information in tandem for embedding and visualizing high-dimensional data. © 2023, CC BY.

关键词： RNA

Bayesian inference for latent chain graphs

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Lu, Deng De Iorio, Maria Jasra, Ajay Rosner, Gary L. Department of Statistics & Applied Probability National University of Singapore Singapore117546 Singapore Yale-NUS Singapore138527 Singapore Department of Statistical Science University College London United Kingdom Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Oncology Biostatistics and Bioinformatics Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University BaltimoreMD21205 United States

In this article we consider Bayesian inference for partially observed Andersson-Madigan-Perlman (AMP) Gaussian chain graph (CG) models. Such models are of particular interest in applications such as biological networks and financial time series. The model itself features a variety of constraints which make both prior modeling and computational inference challenging. We develop a framework for the aforementioned challenges, using a sequential Monte Carlo (SMC) method for statistical inference. Our approach is illustrated on both simulated data as well as real case studies from university graduation rates and a pharmacokinetics study. Copyright © 2019, The Authors. All rights reserved.

关键词： Monte Carlo methods