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Bridging the collaboration gap: Real-time identification of clinical specimens for biomedical research

Journal of Pathology Informatics 2020年第1期11卷 1-7页

作者： Durant, Thomas J.S. Gong, Guannan Price, Nathan Schulz, Wade Department of Laboratory Medicine Yale University School of Medicine New Haven CT United States Center for Outcomes Research and Evaluation Yale New Haven Hospital New Haven CT United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University School of Medicine New Haven CT United States Department of Information Technology Yale New Haven Health New Haven CT United States

Introduction: Biomedical and translational research often relies on the evaluation of patients or specimens that meet specific clinical or laboratory criteria. The typical approach used to identify biospecimens is a manual, retrospective process that exists outside the clinical workflow. This often makes biospecimen collection cost prohibitive and prevents the collection of analytes with short stability times. Emerging data architectures offer novel approaches to enhance specimen-identification practices. To this end, we present a new tool that can be deployed in a real-time environment to automate the identification and notification of available biospecimens for biomedical research. Methods: Real-time clinical and laboratory data from Cloverleaf (Infor, NY, NY) were acquired within our computational health platform, which is built on open-source applications. Study-specific filters were developed in NiFi (Apache Software Foundation, Wakefield, MA, USA) to identify the study-appropriate specimens in real time. Specimen metadata were stored in Elasticsearch (Elastic N. V., Mountain View, CA, USA) for visualization and automated alerting. Results: Between June 2018 and December 2018, we identified 2992 unique specimens belonging to 2815 unique patients, split between two different use cases. Based on laboratory policy for specimen retention and study-specific stability requirements, secure E-mail notifications were sent to investigators to automatically notify of availability. The assessment of throughput on commodity hardware demonstrates the ability to scale to approximately 2000 results per second. Conclusion: This work demonstrates that real-world clinical data can be analyzed in real time to increase the efficiency of biospecimen identification with minimal overhead for the clinical laboratory. Future work will integrate additional data types, including the analysis of unstructured data, to enable more complex cases and biospecimen identification. © 2020 Journal

关键词： Biobanking biomedical research biospecimen science clinical specimens real-time identification translational research

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Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation

Research Square

引用

Research Square 2021年

作者： Freedman, Matthew L. Baca, Sylvan C. Singler, Cassandra Zacharia, Soumya Seo, Ji-Heui Morova, Tunc Hach, Faraz Ding, Yi Schwarz, Tommer Huang, Chia-Chi Flora Kalita, Cynthia Groha, Stefan Pomerantz, Mark M. Wang, Victoria Linder, Simon Sweeney, Christopher J. Zwart, Wilbert Lack, Nathan A. Pasaniuc, Bogdan Takeda, David Y. Gusev, Alexander Department of Medical Oncology Dana-Farber Cancer Institute BostonMA United States Center for Functional Cancer Epigenetics Dana-Farber Cancer Institute BostonMA United States The Eli and Edythe L. Broad Institute CambridgeMA United States Laboratory of Genitourinary Cancer Pathogenesis Center for Cancer Research National Cancer Institute BethesdaMD United States Vancouver Prostate Centre University of British Columbia VancouverBC Canada Bioinformatics Interdepartmental Program UCLA Los AngelesCA United States Division of Genetics Brigham & Women’s Hospital BostonMA United States Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute BostonMA United States Department of Biostatistics Harvard School of Public Health BostonMA United States Division of Oncogenomics Oncode Institute The Netherlands Cancer Institute Amsterdam Netherlands Laboratory of Chemical Biology Institute for Complex Molecular Systems Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Medicine Koç University Istanbul Turkey Department of Computational Medicine David Geffen School of Medicine at UCLA Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine at UCLA Los AngelesCA United States

Methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs), are widely used to functionally annotate trait-associated variants, but they are limited in identifying context-dependent effects on transcription. To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for nominating variants that impact traits through their effects on chromatin state. CWAS associates the genetic determinants of cistromes (e.g., the genome-wide profiles of transcription factor binding sites or histone modifications) with traits using summary statistics from genome-wide association studies (GWAS). We performed CWASs of prostate cancer and androgen-related traits, using a reference panel of 307 prostate cistromes from 165 individuals. CWAS nominated susceptibility regulatory elements or androgen receptor (AR) binding sites at 52 out of 98 known prostate cancer GWAS loci and implicated an additional 17 novel loci. We functionally validated a subset of our results using CRISPRi and in vitro reporter assays. At 28 of the 52 risk loci, CWAS identified regulatory mechanisms that are not observable via eQTLs, implicating genes with complex or context-specific regulation that are overlooked by current approaches that relying on steady-state transcript measurements. CWAS genes include transcription factors that govern prostate development such as NKX3-1, HOXB13, GATA2, and KLF5. Moreover, CWAS boosts discovery power in modestly sized GWAS, identifying novel genetic associations mediated through AR binding for androgen-related phenotypes, including resistance to prostate cancer therapy. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting context-dependent transcriptional regulation. © 2021, CC BY.

关键词： Transcription

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A pathway-based kernel boosting method for sample classification using genomic data

arXiv

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arXiv 2018年

作者： Zeng, Li Yu, Zhaolong Zhao, Hongyu Department of Biostatistics Yale University New HavenCT06511 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States

The analysis of cancer genomic data has long suffered "the curse of dimensionality". Sample sizes for most cancer genomic studies are a few hundreds at most while there are tens of thousands of genomic features studied. Various methods have been proposed to leverage prior biological knowledge, such as pathways, to more effectively analyze cancer genomic data. Most of the methods focus on testing marginal significance of the associations between pathways and clinical phenotypes. They can identify relevant pathways, but do not involve predictive modeling. In this article, we propose a Pathway-based Kernel Boosting (PKB) method for integrating gene pathway information for sample classification, where we use kernel functions calculated from each pathway as base learners and learn the weights through iterative optimization of the classifica- tion loss function. We apply PKB and several competing methods to three cancer studies with pathological and clinical information, including tumor grade, stage, tumor sites, and metastasis status. Our results show that PKB outperforms other methods, and identifies pathways relevant to the outcome variables. Copyright © 2018, The Authors. All rights reserved.

关键词： Genes

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Formalizing the representation of immune exposures for human immunology studies 9

Formalizing the representation of immune exposures for human...

引用

9th International Conference on Biological Ontology, ICBO 2018

作者： Vita, Randi Overton, James A. Cheung, Kei-Hoi Kleinstein, Steven H. Peters, Bjoern Division for Vaccine Discovery San Diego Institute for Allergy and Immunology San DiegoCA United States Department of Emergency Medicine Yale Center for Medical Informatics Yale School of Medicine New HavenCT United States Department of Pathology Yale School of Medicine New HavenCT United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT United States

Human immunology studies typically examine how immune exposures associated with vaccinations, infectious, allergic or autoimmune diseases, or transplantations perturb the immune system with the goal to develop diagnostic tools and therapeutic interventions. While there are established approaches to formally represent the experimental data generated in such studies, which often comprises gene expression data, flow cytometry data, or serology data, the description of the immune exposures themselves is not well standardized. We here present a formal approach to represent immune exposures at a high level of granularity. We capture the exposure process (e.g. 'vaccination' or 'occurrence of allergic disease'), exposure material (e.g. 'Tdap vaccine' or 'House dust mite'), and the associated disease name and stage (e.g. 'allergic rhinitis' and 'chronic'). This representation scheme has been used successfully in the IEDB and an extended version has been adopted by HIPC to capture studies in ImmPort. We are reporting here on this scheme, our ongoing attempts to map the terms used to existing ontologies, and the challenges encountered. © 2018 CEUR-WS.

关键词： Ontology

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Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Research Square

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Research Square 2021年

作者： Zhang, Martin Jinye Hou, Kangcheng Pasaniuc, Bogdan Price, Alkes L. Dey, Kushal K. Jagadeesh, Karthik A. Weinand, Kathryn Sakaue, Saori Taychameekiatchai, Aris Rao, Poorvi Pisco, Angela Oliveira Zou, James Wang, Bruce Gandal, Michael Raychaudhuri, Soumya Harvard University United States Osaka University Japan Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Center for Data Sciences Brigham and Women’s Hospital BostonMA United States Division of Genetics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Division of Rheumatology Inflammation and Immunity Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Medicine and Liver Center University of California San Francisco San FranciscoCA United States Developmental and Stem Cell Biology Graduate Program University of California San Francisco San FranciscoCA United States Chan Zuckerberg Biohub San FranciscoCA United States Department of Electrical Engineering Stanford University Palo AltoCA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Department of Psychiatry David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Program in Neurobehavioral Genetics Semel Institute David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Versus Arthritis Centre for Genetics and Genomic

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution;scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =341K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states;subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region;and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes. © 2021, CC BY.

关键词： RNA

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Serotonin transporter inhibits antitumor immunity through regulating the intratumoral serotonin axis

引用

Cell 2025年

作者： Bo Li James Elsten-Brown Miao Li Enbo Zhu Zhe Li Yuning Chen Elliot Kang Feiyang Ma Jennifer Chiang Yan-Ruide Li Yichen Zhu Jie Huang Audrey Fung Quentin Scarborough Robin Cadd Jin J Zhou Arnold I Chin Matteo Pellegrini Lili Yang Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA 90095 USA. Electronic address: bo.li@ucla.edu. Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA 90095 USA. Department of Materials Science and Engineering University of California Los Angeles Los Angeles CA 90095 USA. Department of Molecular Cell and Developmental Biology University of California Los Angeles Los Angeles CA 90095 USA. Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles CA USA. Department of Urology University of California Los Angeles Los Angeles CA 90095 USA Eli & Edythe Broad Center of Regenerative Medicine and Stem Cell Research University of California Los Angeles Los Angeles CA 90095 USA Jonsson Comprehensive Cancer Center David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Department of Molecular Cell and Developmental Biology University of California Los Angeles Los Angeles CA 90095 USA Bioinformatics Interdepartmental Program University of California Los Angeles Los Angeles CA 90095 USA Institute for Quantitative and Computational Biosciences-The Collaboratory University of California Los Angeles Los Angeles CA 90095 USA. Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA 90095 USA Jonsson Comprehensive Cancer Center David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Bioengineering University of California Los Angeles Los Angeles CA 90095 USA Goodman-Luskin Microbiome Center University of California Los Angeles Los Angeles CA 90095 USA Molecular Biology Institute University of California Los Angeles Los Angeles CA 90095 USA Parker Institute for Cancer Immunotherapy University of California Los Angeles Los Angel

Identifying additional immune checkpoints hindering antitumor T cell responses is key to the development of next-generation cancer immunotherapies. Here, we report the induction of serotonin transporter (SERT), a regulator of serotonin levels and physiological functions in the brain and peripheral tissues, in tumor-infiltrating CD8 T cells. Inhibition of SERT using selective serotonin reuptake inhibitors (SSRIs), the most widely prescribed antidepressants, significantly suppressed tumor growth and enhanced T cell antitumor immunity in various mouse syngeneic and human xenograft tumor models. Importantly, SSRI treatment exhibited significant therapeutic synergy with programmed cell death protein 1 (PD-1) blockade, and clinical data correlation studies negatively associated intratumoral SERT expression with patient survival in a range of cancers. Mechanistically, SERT functions as a negative-feedback regulator inhibiting CD8 T cell reactivities by depleting intratumoral T cell-autocrine serotonin. These findings highlight the significance of the intratumoral serotonin axis and identify SERT as an immune checkpoint, positioning SSRIs as promising candidates for cancer immunotherapy.

关键词： PD-1 blockade SSRIs T cell antitumor immunity antidepressants cancer immunotherapy immune checkpoint intratumoral serotonin axis serotonin serotonin transporter tumor microenvironment

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Technology dictates algorithms: Recent developments in read alignment

arXiv

引用

arXiv 2020年

作者： Alser, Mohammed Rotman, Jeremy Taraszka, Kodi Shi, Huwenbo Baykal, Pelin Icer Yang, Harry Taegyun Xue, Victor Knyazev, Sergey Singer, Benjamin D. Balliu, Brunilda Koslicki, David Skums, Pavel Zelikovsky, Alex Alkan, Can Mutlu, Onur Mangul, Serghei Computer Science Department ETH Zürich Zürich8092 Switzerland Computer Engineering Department Bilkent University Bilkent Ankara 06800 Turkey Department of Computer Science University of California Los Angeles Los AngelesCA90095 United States Department of Epidemiology Harvard T.H. Chan School of Public Health MA02115 United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA02142 United States Department of Computer Science Georgia State University AtlantaGA30302 United States Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles Los AngelesCA90095 United States Division of Pulmonary and Critical Care Medicine Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Biochemistry & Molecular Genetics Northwestern University Feinberg School of Medicine United States Simpson Querrey Center for Epigenetics Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Computer Science and Engineering Pennsylvania State University University ParkPA16801 United States Biology Department Pennsylvania State University University ParkPA16801 United States The Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16801 United States The Laboratory of Bioinformatics I.M. Sechenov First Moscow State Medical University Moscow119991 Russia Bilkent-Hacettepe Health Sciences and Technologies Program Ankara Turkey Department of Clinical Pharmacy School of Pharmacy University of Southern California Los AngelesCA90089 United States

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to today’s diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies. Copyright © 2020, The Authors. All rights reserved.

关键词： bioinformatics

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Correction to: Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes

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Genome medicine 2021年第1期13卷 139页

作者： David Z Pan Zong Miao Caroline Comenho Sandhya Rajkumar Amogha Koka Seung Hyuk T Lee Marcus Alvarez Dorota Kaminska Arthur Ko Janet S Sinsheimer Karen L Mohlke Nicholas Mancuso Linda Liliana Muñoz-Hernandez Miguel Herrera-Hernandez Maria Teresa Tusié-Luna Carlos Aguilar-Salinas Kirsi H Pietiläinen Jussi Pihlajamäki Markku Laakso Kristina M Garske Päivi Pajukanta Department of Human Genetics David Geffen School of Medicine at UCLA Los Angeles USA. Bioinformatics Interdepartmental Program UCLA Los Angeles USA. Computational and Systems Biology Interdepartmental Program UCLA Los Angeles USA. Institute of Public Health and Clinical Nutrition University of Eastern Finland Kuopio Finland. Department of Medicine David Geffen School of Medicine at UCLA Los Angeles USA. Department of Computational Medicine David Geffen School of Medicine at UCLA Los Angeles USA. Department of Genetics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA. Center for Genetic Epidemiology Department of Preventative Medicine Keck School of Medicine University of Southern California Los Angeles USA. Tecnologico de Monterrey Escuela de Medicina y Ciencias de la Salud Ave. Morones Prieto 3000 Monterrey N.L. México 64710. Unidad de Investigación de Enfermedades Metabólicas Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Departamento de Endocrinología y Metabolismo del Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Departamento de Cirugía Instituto Nacional de Ciencias Médicas y Nutrición Mexico City Mexico. Unidad de Biología Molecular y Medicina Genómica Instituto de Investigaciones Biomédicas UNAM/ Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Obesity Research Unit Research Program for Clinical and Molecular Metabolism Faculty of Medicine University of Helsinki Helsinki Finland. Obesity Center Endocrinology Abdominal Center Helsinki University Central Hospital and University of Helsinki Helsinki Finland. Department of Medicine Endocrinology and Clinical Nutrition Kuopio University Hospital Kuopio Finland. Department of Medicine University of Eastern Finland and Kuopio University Hospital Kuopio Finland. Department of Human Genetics David Geffen School of Medicine at UCLA Los Angeles USA. ppajukant

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Author Correction: Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative

引用

Genome medicine 2022年第1期14卷 128页

作者： Ruth Johnson Yi Ding Vidhya Venkateswaran Arjun Bhattacharya Kristin Boulier Alec Chiu Sergey Knyazev Tommer Schwarz Malika Freund Lingyu Zhan Kathryn S Burch Christa Caggiano Brian Hill Nadav Rakocz Brunilda Balliu Christopher T Denny Jae Hoon Sul Noah Zaitlen Valerie A Arboleda Eran Halperin Sriram Sankararaman Manish J Butte Clara Lajonchere Daniel H Geschwind Bogdan Pasaniuc Department of Computer Science University of California Los Angeles Los Angeles CA 90095 USA. ruthjohnson@g.ucla.edu. Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. ruthjohnson@g.ucla.edu. Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Bioinformatics Interdepartmental Program University of California Los Angeles Los Angeles CA 90095 USA. Department of Oral Biology School of Dentistry University of California Los Angeles Los Angeles CA 90095 USA. Institute for Quantitative and Computational Biosciences David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Department of Medicine Division of Cardiology University of California Los Angeles Los Angeles CA 90095 USA. Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Department of Genetics Stanford School of Medicine Stanford CA 94305 USA. Molecular Biology Institute David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Program in Neurogenetics Department of Neurology David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Department of Computer Science University of California Los Angeles Los Angeles CA 90095 USA. Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Division of Hematology/Oncology Department of Pediatrics Gwynne Hazen Cherry Memorial Laboratories University of California Los Angeles Los Angeles CA 90095 USA. Molecular Biology Institute University of California Los Angeles Los Angeles CA 90095 USA. Jonsson Comprehensive Cancer Center University of California Los Angeles Los Angeles CA 90095

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Learning binary latent variable models: A tensor eigenpair approach

arXiv

引用

arXiv 2018年

作者： Jaffe, Ariel Weiss, Roi Carmi, Shai Kluger, Yuval Nadler, Boaz Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot7610001 Israel Braun School of Public Health and Community Medicine Hebrew University of Jerusalem Jerusalem9112102 Israel Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Dept. of Pathology Yale Cancer Center Yale University School of Medicine New HavenCT06520 United States Program of Applied Mathematics Yale University New HavenCT06511 United States

Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and third order moment tensor of the observed data. We prove that under mild non-degeneracy conditions, our method consistently estimates the model parameters at the optimal parametric rate. Our tensor-based method generalizes previous orthogonal tensor decomposition approaches, where the hidden units were assumed to be either statistically independent or mutually exclusive. We illustrate the consistency of our method on simulated data and demonstrate its usefulness in learning a common model for population mixtures in genetics. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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