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Author Correction: A case-only study to identify genetic modifiers of breast cancer risk for BRCA1/BRCA2 mutation carriers

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Nature communications 2021年第1期12卷 2986页

作者： Juliette Coignard Michael Lush Jonathan Beesley Tracy A O'Mara Joe Dennis Jonathan P Tyrer Daniel R Barnes Lesley McGuffog Goska Leslie Manjeet K Bolla Muriel A Adank Simona Agata Thomas Ahearn Kristiina Aittomäki Irene L Andrulis Hoda Anton-Culver Volker Arndt Norbert Arnold Kristan J Aronson Banu K Arun Annelie Augustinsson Jacopo Azzollini Daniel Barrowdale Caroline Baynes Heko Becher Marina Bermisheva Leslie Bernstein Katarzyna Białkowska Carl Blomqvist Stig E Bojesen Bernardo Bonanni Ake Borg Hiltrud Brauch Hermann Brenner Barbara Burwinkel Saundra S Buys Trinidad Caldés Maria A Caligo Daniele Campa Brian D Carter Jose E Castelao Jenny Chang-Claude Stephen J Chanock Wendy K Chung Kathleen B M Claes Christine L Clarke J Margriet Collée Don M Conroy Kamila Czene Mary B Daly Peter Devilee Orland Diez Yuan Chun Ding Susan M Domchek Thilo Dörk Isabel Dos-Santos-Silva Alison M Dunning Miriam Dwek Diana M Eccles A Heather Eliassen Christoph Engel Mikael Eriksson D Gareth Evans Peter A Fasching Henrik Flyger Florentia Fostira Eitan Friedman Lin Fritschi Debra Frost Manuela Gago-Dominguez Susan M Gapstur Judy Garber Vanesa Garcia-Barberan Montserrat García-Closas José A García-Sáenz Mia M Gaudet Simon A Gayther Andrea Gehrig Vassilios Georgoulias Graham G Giles Andrew K Godwin Mark S Goldberg David E Goldgar Anna González-Neira Mark H Greene Pascal Guénel Lothar Haeberle Eric Hahnen Christopher A Haiman Niclas Håkansson Per Hall Ute Hamann Patricia A Harrington Steven N Hart Wei He Frans B L Hogervorst Antoinette Hollestelle John L Hopper Darling J Horcasitas Peter J Hulick David J Hunter Evgeny N Imyanitov Agnes Jager Anna Jakubowska Paul A James Uffe Birk Jensen Esther M John Michael E Jones Rudolf Kaaks Pooja Middha Kapoor Beth Y Karlan Renske Keeman Elza Khusnutdinova Johanna I Kiiski Yon-Dschun Ko Veli-Matti Kosma Peter Kraft Allison W Kurian Yael Laitman Diether Lambrechts Loic Le Marchand Jenny Lester Fabienne Lesueur Tricia Lindstrom Adria Lopez-Fernández Jennifer T Loud Craig Luccarini Arto Mannermaa Siranou Genetic Epidemiology of Cancer team Inserm U900 Paris France. Institut Curie Paris Paris France. Mines ParisTech Fontainebleau Paris France. Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care University of Cambridge Cambridge UK. PSL University Paris Paris France. Paris Sud University Orsay France. Department of Genetics and Computational Biology QIMR Berghofer Medical Research Institute Brisbane QLD Australia. Centre for Cancer Genetic Epidemiology Department of Oncology University of Cambridge Cambridge UK. Family Cancer Clinic The Netherlands Cancer Institute Antoni van Leeuwenhoek hospital Amsterdam The Netherlands. Immunology and Molecular Oncology Unit Veneto Institute of Oncology IOV - IRCCS Padua Italy. Division of Cancer Epidemiology and Genetics National Cancer Institute National Institutes of Health Department of Health and Human Services Bethesda MD USA. Department of Clinical Genetics Helsinki University Hospital University of Helsinki Helsinki Finland. Fred A. Litwin Center for Cancer Genetics Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Toronto ON Canada. Department of Molecular Genetics University of Toronto Toronto ON Canada. Department of Epidemiology Genetic Epidemiology Research Institute University of California Irvine Irvine CA USA. Division of Clinical Epidemiology and Aging Research German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Gynaecology and Obstetrics University Hospital of Schleswig-Holstein Campus Kiel Christian-Albrechts University Kiel Kiel Germany. Institute of Clinical Molecular Biology University Hospital of Schleswig-Holstein Campus Kiel Christian-Albrechts University Kiel Kiel Germany. Department of Public Health Sciences and Cancer Research Institute Queen's University Kingston ON Canada. Department of Breast Medical Oncology University of Texas MD Anderson Cancer Center Houston TX USA. Department of Cancer Epidemiology Clinical Sciences

A Correction to this paper has been published: https://***/10.1038/s41467-021-23162-4.

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HBS-Tools for Hairpin Bisulfite Sequencing Data Processing and Analysis

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Advances in bioinformatics 2015年第1期2015卷 760423页

作者： Sun, Ming-An Velmurugan, Karthik Raja Keimig, David Xie, Hehuang Epigenomics and Computational Biology Lab Virginia Bioinformatics Institute Virginia Tech Blacksburg 24061 VA United States Interdisciplinary Ph.D. Program in Genomics Bioinformatics and Computational Biology Virginia Tech Blacksburg 24061 VA United States Department of Biological Sciences Virginia Tech Blacksburg 24061 VA United States

The emerging genome-wide hairpin bisulfite sequencing (hairpin-BS-Seq) technique enables the determination of the methylation pattern for DNA double strands simultaneously. Compared with traditional bisulfite sequencing (BS-Seq) techniques, hairpin-BS-Seq can determine methylation fidelity and increase mapping efficiency. However, no computational tool has been designed for the analysis of hairpin-BS-Seq data yet. Here we present HBS-tools, a set of command line based tools for the preprocessing, mapping, methylation calling, and summarizing of genome-wide hairpin-BS-Seq data. It accepts paired-end hairpin-BS-Seq reads to recover the original (pre-bisulfite-converted) sequences using global alignment and then calls the methylation statuses for cytosines on both DNA strands after mapping the original sequences to the reference genome. After applying to hairpin-BS-Seq datasets, we found that HBS-tools have a reduced mapping time and improved mapping efficiency compared with state-of-the-art mapping tools. The HBS-tools source scripts, along with user guide and testing data, are freely available for download. © 2015 Ming-an Sun et al.

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Cover Image, Volume 84, Issue 12

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Proteins: Structure, Function, and bioinformatics 2016年第12期84卷

作者： Hyuntae Na Guang Song Department of Computer Science Penn State Harrisburg Middletown Pennsylvania 17057 Department of Computer Science Iowa State University Ames Iowa 50011 Program of Bioinformatics and Computational Biology Iowa State University Ames Iowa 50011 L. H. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames Iowa 50011

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Donor Selection for Hematopoietic Stem Cell Transplant Using Cost-Sensitive SVM

Donor Selection for Hematopoietic Stem Cell Transplant Using...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Adarsh Sivasankaran Vladimir Cherkassky Mark Albrecht Eric Williams Martin Maiers Biomedical Informatics and Computational Biology Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota U.S.A. Bioinformatics Research National Marrow Donor Program Minneapolis Minnesota U.S.A.

ISBN: (纸本)9781509002887

Donor selection for Hematopoietic Stem Cell Transplant often requires physicians to manually select 3 to 5 donors from a list of 100s of genetically compatible donors as identified by HLA-based matching algorithms. The decision process is complicated by a lack of strict guidelines governing a "secondary" selection process, which is based upon non-HLA donor attributes. Our research is aimed at modeling this "secondary" decision process which can help physicians choose the right donors, based on donor attributes and historical choice behavior. Proposed black box models will help in improving selection consistency.

关键词： Medical services Support vector machines Data models Stem cells Mathematical model Numerical models Training

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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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P3033 A comprehensive porcine blood transcriptome

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Journal of Animal Science 2016年第SUPPL_4期94卷 69-69页

作者： Liu, H. Smith, T. P. L. Nonneman, D. J. Dekkers, J. C. M. Tuggle, C. K. Bioinformatics and Computational Biology Program Department of Animal Science Iowa State University Ames IA USDA ARS U.S. Meat Animal Research Center Clay Center NE Department of Animal Science Iowa State University Ames IA

Blood sample analyses are extensively used in high throughput assays in biomedicine, as well as animal genetics and physiology research. However, the draft quality of the current pig genome (Sscrofa 10.2) is insufficient for accurate interpretation of many of these assays because of incomplete gene and transcript isoform annotations. In this study, we assembled a comprehensive blood de novo transcriptome by using the Trinity platform on 162,285,683 pairs of paired-end and 183,116,578 single-end, clean and normalized Illumina reads (25 to 100 bases in length) from five independent RNA-seq studies of pig blood. This raw assembly consisted of 490,209 putative transcripts (PTs) from 397,560 genomic loci and includes more than 97% of the normalized reads. To verify the porcine origin of these assembled PTs, we mapped them to a PacBio long read-based USMARC pig genome assembly (T. Smith et al., unpubl.) and to the Sscrofa 10.2 assembly. Overall, 99.4% and 94.2% of PTs could be mapped to the USMARC and Sscrofa 10.2 assemblies, respectively, with more than 97% coverage. Notably, the majority of 3089 PTs that could not be mapped to the USMARC assembly were of bacterial, viral or plant origin. We removed unmapped PTs that did not align to mammalian sequences in the NCBI nt database and filtered the PTs using relaxed criteria based on minimum expression level, length and splicing potential, producing a set of 159,146 unique PTs, 121,057 of which were putative spliced transcripts. We aligned the reduced set of PTs along with a newly available dataset of IsoSeq transcripts from pig liver, spleen and thymus (H. Liu et al., unpubl.) to the current Sscrofa 10.2 genome assembly. Visual inspection of these alignments in IGV showed that a large number of the assembled blood transcripts were structurally more complete/accurate than their counterparts in the Sscrofa 10.2 annotation. We will report further filtering, validation and annotation of these PT, including alignment to IsoSeq-de

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Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

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Nature communications 2023年第1期14卷 3043页

作者： Jianxin Shi Kouya Shiraishi Jiyeon Choi Keitaro Matsuo Tzu-Yu Chen Juncheng Dai Rayjean J Hung Kexin Chen Xiao-Ou Shu Young Tae Kim Maria Teresa Landi Dongxin Lin Wei Zheng Zhihua Yin Baosen Zhou Bao Song Jiucun Wang Wei Jie Seow Lei Song I-Shou Chang Wei Hu Li-Hsin Chien Qiuyin Cai Yun-Chul Hong Hee Nam Kim Yi-Long Wu Maria Pik Wong Brian Douglas Richardson Karen M Funderburk Shilan Li Tongwu Zhang Charles Breeze Zhaoming Wang Batel Blechter Bryan A Bassig Jin Hee Kim Demetrius Albanes Jason Y Y Wong Min-Ho Shin Lap Ping Chung Yang Yang She-Juan An Hong Zheng Yasushi Yatabe Xu-Chao Zhang Young-Chul Kim Neil E Caporaso Jiang Chang James Chung Man Ho Michiaki Kubo Yataro Daigo Minsun Song Yukihide Momozawa Yoichiro Kamatani Masashi Kobayashi Kenichi Okubo Takayuki Honda Dean H Hosgood Hideo Kunitoh Harsh Patel Shun-Ichi Watanabe Yohei Miyagi Haruhiko Nakayama Shingo Matsumoto Hidehito Horinouchi Masahiro Tsuboi Ryuji Hamamoto Koichi Goto Yuichiro Ohe Atsushi Takahashi Akiteru Goto Yoshihiro Minamiya Megumi Hara Yuichiro Nishida Kenji Takeuchi Kenji Wakai Koichi Matsuda Yoshinori Murakami Kimihiro Shimizu Hiroyuki Suzuki Motonobu Saito Yoichi Ohtaki Kazumi Tanaka Tangchun Wu Fusheng Wei Hongji Dai Mitchell J Machiela Jian Su Yeul Hong Kim In-Jae Oh Victor Ho Fun Lee Gee-Chen Chang Ying-Huang Tsai Kuan-Yu Chen Ming-Shyan Huang Wu-Chou Su Yuh-Min Chen Adeline Seow Jae Yong Park Sun-Seog Kweon Kun-Chieh Chen Yu-Tang Gao Biyun Qian Chen Wu Daru Lu Jianjun Liu Ann G Schwartz Richard Houlston Margaret R Spitz Ivan P Gorlov Xifeng Wu Ping Yang Stephen Lam Adonina Tardon Chu Chen Stig E Bojesen Mattias Johansson Angela Risch Heike Bickeböller Bu-Tian Ji H-Erich Wichmann David C Christiani Gadi Rennert Susanne Arnold Paul Brennan James McKay John K Field Sanjay S Shete Loic Le Marchand Geoffrey Liu Angeline Andrew Lambertus A Kiemeney Shan Zienolddiny-Narui Kjell Grankvist Mikael Johansson Angela Cox Fiona Taylor Jian-Min Yuan Philip Lazarus Matthew B Schabath Melinda C Aldrich Hyo-Sung Jeon Shih Sheng Jiang Jae Sook Sung C Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. jianxin.shi@nih.gov. Division of Genome Biology National Cancer Research Institute Tokyo Japan. Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. Division of Cancer Epidemiology and Prevention Aichi Cancer Center Research Institute Nagoya Japan. Institute of Population Health Sciences National Health Research Institutes Zhunan Taiwan. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Jiangsu Key Lab of Cancer Biomarkers Prevention and Treatment Collaborative Innovation Center for Cancer Medicine Nanjing Medical University Nanjing China. Prosserman Centre for Population Health Research Lunenfeld-Tanenbaum Research Institute Sinai Health Toronto ON Canada. Department of Epidemiology and Biostatistics National Clinical Research Center for Cancer Key Laboratory of Molecular Cancer Epidemiology of Tianjin Tianjin Medical University Cancer Institute and Hospital Tianjin Medical University Tianjin China. Division of Epidemiology Department of Medicine Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center Nashville TN USA. Cancer Research Institute Seoul National University College of Medicine Seoul Republic of Korea. Department of Etiology & Carcinogenesis and State Key Laboratory of Molecular Oncology Cancer Institute and Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China. Department of Epidemiology School of Public Health China Medical University Shenyang China. Department of Clinical Epidemiology and Center of Evidence Based Medicine The First Hospital of China Medical University Shenyang China. Department of Oncology Shandong Cancer Hospital and Institute Shandong Academy of Medical Sciences Jinan China. Ministry of Education Key Laboratory of Contemporary Anthropology School of Life Sciences Fudan University Shanghai China. S

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

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Erratum to: The real cost of sequencing: scaling computation to keep pace with data generation

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Genome biology 2016年第1期17卷 78页

作者： Paul Muir Shantao Li Shaoke Lou Daifeng Wang Daniel J Spakowicz Leonidas Salichos Jing Zhang George M Weinstock Farren Isaacs Joel Rozowsky Mark Gerstein Department of Molecular Cellular and Developmental Biology Yale University New Haven CT 06520 USA. Systems Biology Institute Yale University West Haven CT 06516 USA. Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. The Jackson Laboratory for Genomic Medicine Farmington CT 06032 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark@***. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark@***. Department of Computer Science Yale University New Haven CT 06520 USA. mark@***.

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Constructing a Boolean implication network to study the interactions between environmental factors and OTUs

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Frontiers of Electrical and Electronic Engineering in China 2014年第4期9卷 127-141页

作者： Congmin Zhu Rui Jiang Ting Chen MOE Key Laboratory of Bioinformatics and Bioinformatics Division Center for Synthetic & Systems Biology TNLIST/ Department of Automation Tsinghua University Beijing 100084 China Computational and Molecular Biology Program University of Southern California Loa Angles CA 90089 USA

Mining relationships between microbes and the environment they live in are crucial to understand the intrinsic mechanisms that govern cycles of carbon, nitrogen and energy in a microbial community. Building upon next- generation sequencing technology, the selective capture of 16S rRNA genes has enabled the study of co-occurrence patterns of microbial species from the viewpoint of complex networks, yielding successful descriptions of phenomena exhibited in a microbial community. However, since the effects of such environmental factors as temperature or soil conditions on microbes are complex, reliance on the analysis of co-occurrence networks alone cannot elucidate such complicated effects underlying microbial communities. In this study, we apply a statistical method, which is called Boolean implications for metagenomic studies （BIMS） for extracting Boolean implications （IF-THEN relationships） to capture the effects of environmental factors on microbial species based on 16S rRNA sequencing data. We first demonstrate the power and effectiveness of BIMS through comprehensive simulation studies and then apply it to a 16S rRNA sequencing dataset of real marine microbes. Based on a total of 6,514 pairwise relationships identified at a low false discovery rate （FDR） of 0.01, we construct a Boolean implication network between operational taxonomic units （OTUs） and environmental factors. Relationships in this network are supported by literature, and, most importantly, they bring biological insights into the effects of environmental factors on microbes. We next apply BIMS to detect three-way relationships and show the possibility of using this strategy to explain more complex relationships within a microbial community.

关键词： Boolean implication metagenome marine OTUs environmental factors

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Bayesian group factor analysis with structured sparsity

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Shiwen Zhao Chuan Gao Sayan Mukherjee Barbara E. Engelhardt Google MPI for Intelligent Systems Computational Biology and Bioinformatics Program Department of Statistical Science Duke University Durham NC Department of Statistical Science Duke University Durham NC Departments of Statistical Science Computer Science Mathematics Duke University Durham NC Department of Computer Science Center for Statistics and Machine Learning Princeton University Princeton NJ

Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for a matrix of p features across n samples. We develop a structured Bayesian group factor analysis model that extends the factor model to multiple coupled observation matrices; in the case of two observations, this reduces to a Bayesian model of canonical correlation analysis. Here, we carefully define a structured Bayesian prior that encourages both element-wise and column-wise shrinkage and leads to desirable behavior on high-dimensional data. In particular, our model puts a structured prior on the joint factor loading matrix, regularizing at three levels, which enables element-wise sparsity and unsupervised recovery of latent factors corresponding to structured variance across arbitrary subsets of the observations. In addition, our structured prior allows for both dense and sparse latent factors so that covariation among either all features or only a subset of features can be recovered. We use fast parameter-expanded expectation-maximization for parameter estimation in this model. We validate our method on simulated data with substantial structure. We show results of our method applied to three high-dimensional data sets, comparing results against a number of state-of-the-art approaches. These results illustrate useful properties of our model, including i) recovering sparse signal in the presence of dense effects; ii) the ability to scale naturally to large numbers of observations; iii) flexible observation- and factor-specific regularization to recover factors with a wide variety of sparsity levels and percentage of variance explained; and iv) tractable inference that scales to modern genomic and text data sizes.

关键词： bayesian structured sparsity canonical correlation analysis mixture models parameter expansion sparse and low-rank matrix decomposition sparse priors

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