Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data
暂无分享,去创建一个
Giri Narasimhan | Ziv Bar-Joseph | Jose Lugo-Martinez | Daniel Ruiz-Perez | Kalai Mathee | Betiana Lerner | Natalia Bourguignon | Z. Bar-Joseph | Jose Lugo-Martinez | G. Narasimhan | K. Mathee | D. Ruiz-Perez | N. Bourguignon | B. Lerner | Natalia Bourguignon
[1] D. Anderson. HABs in a changing world: a perspective on harmful algal blooms, their impacts, and research and management in a dynamic era of climactic and environmental change. , 2014, Harmful algae 2012 : proceedings of the 15th International Conference on Harmful Algae : October 29 - November 2, 2012, CECO, Changwon, Gyeongnam, Korea. International Conference on Harmful Algae (15th : 2012 : Changwon, Gyeongnam, Kore....
[2] Christopher E. McKinlay,et al. Multi-omics analysis of inflammatory bowel disease. , 2014, Immunology letters.
[3] Giri Narasimhan,et al. So you think you can PLS-DA? , 2018 .
[4] Jun Wang,et al. ‘Multi-omic’ data analysis using O-miner , 2017, Briefings Bioinform..
[5] C. Chassard,et al. Assessment of bacterial diversity in breast milk using culture-dependent and culture-independent approaches. , 2013, The British journal of nutrition.
[6] William D. Penny,et al. Comparing Dynamic Causal Models using AIC, BIC and Free Energy , 2012, NeuroImage.
[7] W. Huber,et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.
[8] David J. T. Sumpter,et al. Individual Rules for Trail Pattern Formation in Argentine Ants (Linepithema humile) , 2012, PLoS Comput. Biol..
[9] Kevin S. Bonham,et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases , 2019, Nature.
[10] Mathias Wilhelm,et al. Global proteome analysis of the NCI-60 cell line panel. , 2013, Cell reports.
[11] Casey M. Theriot,et al. Metabolic Model-Based Integration of Microbiome Taxonomic and Metabolomic Profiles Elucidates Mechanistic Links between Ecological and Metabolic Variation , 2016, mSystems.
[12] Georg K Gerber,et al. The dynamic microbiome , 2014, FEBS letters.
[13] A. Butte,et al. The Integrative Human Microbiome Project: Dynamic Analysis of Microbiome-Host Omics Profiles during Periods of Human Health and Disease , 2014, Cell host & microbe.
[14] A. O'Hagan,et al. Kendall's Advanced Theory of Statistics, Vol. 2b: Bayesian Inference. , 1996 .
[15] Peter D. Karp,et al. The EcoCyc and MetaCyc databases , 2000, Nucleic Acids Res..
[16] Robert F. Murphy,et al. Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing , 2010, Bioinform..
[17] Shuzhao Li,et al. Network-Based Approaches for Multi-omics Integration. , 2020, Methods in molecular biology.
[18] Harald Steck,et al. Learning the Bayesian Network Structure: Dirichlet Prior versus Data , 2008, UAI 2008.
[19] Sebastián M. Real,et al. E2F1 Regulates Cellular Growth by mTORC1 Signaling , 2011, PloS one.
[20] Thomas Schiex,et al. Gene Regulatory Network Reconstruction Using Bayesian Networks, the Dantzig Selector, the Lasso and Their Meta-Analysis , 2011, PloS one.
[21] Insuk Lee,et al. A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality , 2007, BMC Bioinformatics.
[22] Samuel B. Fey,et al. The under‐ice microbiome of seasonally frozen lakes , 2013 .
[23] C. Huttenhower,et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease , 2018, Nature Microbiology.
[24] Greg W. Clark,et al. Panorama of ancient metazoan macromolecular complexes , 2015, Nature.
[25] N. Wermuth,et al. Graphical Models for Associations between Variables, some of which are Qualitative and some Quantitative , 1989 .
[26] Harald Steck,et al. Learning the Bayesian Network Structure: Dirichlet Prior vs Data , 2008, UAI.
[27] Edward L. Huttlin,et al. The BioPlex Network: A Systematic Exploration of the Human Interactome , 2015, Cell.
[28] Lorenzo Beretta,et al. Nearest neighbor imputation algorithms: a critical evaluation , 2016, BMC Medical Informatics and Decision Making.
[29] G. von Heijne,et al. Tissue-based map of the human proteome , 2015, Science.
[30] Eran Elinav,et al. Use of Metatranscriptomics in Microbiome Research , 2016, Bioinformatics and biology insights.
[31] Alexander J. Hartemink,et al. Learning Non-Stationary Dynamic Bayesian Networks , 2010, J. Mach. Learn. Res..
[32] Scott T. Weiss,et al. CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data , 2014, PLoS Comput. Biol..
[33] Kevin P. Murphy,et al. Dynamic Bayesian Networks for Audio-Visual Speech Recognition , 2002, EURASIP J. Adv. Signal Process..
[34] Cranos M. Williams,et al. Predicting gene regulatory networks by combining spatial and temporal gene expression data in Arabidopsis root stem cells , 2017, Proceedings of the National Academy of Sciences.
[35] P. Christie. The Mosaic Type IV Secretion Systems. , 2016, EcoSal Plus.
[36] Giri Narasimhan,et al. So you think you can PLS-DA? , 2017, BMC Bioinformatics.
[37] Lawrence A. David,et al. A phylogenetic transform enhances analysis of compositional microbiota data , 2016, bioRxiv.
[38] J. H. van de Wijgert,et al. A fruitful alliance: the synergy between Atopobium vaginae and Gardnerella vaginalis in bacterial vaginosis-associated biofilm , 2016, Sexually Transmitted Infections.
[39] Tormod Næs,et al. Characterizing mixed microbial population dynamics using time-series analysis , 2008, The ISME Journal.
[40] Gregory F. Cooper,et al. The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..
[41] Bernard M. Corfe,et al. Dysbiosis of the gut microbiota in disease , 2015, Microbial ecology in health and disease.
[42] Benoît Iung,et al. Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas , 2012, Eng. Appl. Artif. Intell..
[43] P. Shannon,et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.
[44] Claire D. McWhite,et al. Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes , 2017, Molecular systems biology.
[45] Robert J. Palmer,et al. Communication among Oral Bacteria , 2002, Microbiology and Molecular Biology Reviews.
[46] Simeone Marino,et al. Mathematical modeling of primary succession of murine intestinal microbiota , 2013, Proceedings of the National Academy of Sciences.
[47] N. Stenseth,et al. Convergent temporal dynamics of the human infant gut microbiota , 2010, The ISME Journal.
[48] Daniel L. K. Yamins,et al. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition , 2014, PLoS Comput. Biol..
[49] Russ B. Altman,et al. Missing value estimation methods for DNA microarrays , 2001, Bioinform..
[50] M. Kendall,et al. Kendall's advanced theory of statistics , 1995 .
[51] William D. Shannon,et al. Patterned progression of bacterial populations in the premature infant gut , 2014, Proceedings of the National Academy of Sciences.
[52] Timothy R. Cavagnaro,et al. A Concise Review on Multi-Omics Data Integration for Terroir Analysis in Vitis vinifera , 2017, Front. Plant Sci..
[53] David S. Wishart,et al. HMDB 3.0—The Human Metabolome Database in 2013 , 2012, Nucleic Acids Res..
[54] Peer Bork,et al. Extensive impact of non-antibiotic drugs on human gut bacteria , 2018, Nature.
[55] Luke R. Thompson,et al. Species-level functional profiling of metagenomes and metatranscriptomes , 2018, Nature Methods.
[56] Chi Zhang,et al. A new approach for multi-omic data integration , 2014, 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[57] A. Clark. The Human Microbiome. , 2017, The American journal of nursing.
[58] Susan M. Huse,et al. Microbial diversity in the deep sea and the underexplored “rare biosphere” , 2006, Proceedings of the National Academy of Sciences.
[59] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[60] Ziv Bar-Joseph,et al. DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data , 2012, BMC Systems Biology.
[61] Zaid Abdo,et al. Temporal Dynamics of the Human Vaginal Microbiota , 2012, Science Translational Medicine.
[62] Xavier Daura,et al. Understanding the Molecular Determinants Driving the Immunological Specificity of the Protective Pilus 2a Backbone Protein of Group B Streptococcus , 2013, PLoS Comput. Biol..
[63] Pratik D Jagtap,et al. Multi-omic data analysis using Galaxy , 2015, Nature Biotechnology.
[64] Estelle Glory-Afshar,et al. Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns , 2010, Proceedings of the National Academy of Sciences.
[65] Andreas Wilke,et al. phylogenetic and functional analysis of metagenomes , 2022 .
[66] James T. Van Leuven,et al. Modeling time-series data from microbial communities , 2016, The ISME Journal.
[67] M. Pop,et al. Identification of microbiota dynamics using robust parameter estimation methods. , 2017, Mathematical biosciences.
[68] Mark Craven,et al. Clustered alignments of gene-expression time series data , 2009, Bioinform..
[69] Z. Abdo,et al. Effects of tampons and menses on the composition and diversity of vaginal microbial communities over time , 2013, BJOG : an international journal of obstetrics and gynaecology.
[70] Georg K. Gerber,et al. Inferring Dynamic Signatures of Microbes in Complex Host Ecosystems , 2012, PLoS Comput. Biol..
[71] Travis E. Gibson,et al. Robust and Scalable Models of Microbiome Dynamics , 2018, ICML.
[72] E. Martínez-García,et al. Stationary phase in gram-negative bacteria. , 2010, FEMS microbiology reviews.
[73] Aidong Zhang,et al. Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE) , 2019, BMC Genomics.
[74] P. Poole,et al. The plant microbiome , 2013, Genome Biology.
[75] James T. Morton,et al. Establishing microbial composition measurement standards with reference frames , 2019, Nature Communications.
[76] Marco Y. Hein,et al. A Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances , 2015, Cell.
[77] H. Boyer,et al. A complementation analysis of the restriction and modification of DNA in Escherichia coli. , 1969, Journal of molecular biology.
[78] Patrik D'haeseleer,et al. Linear Modeling of mRNA Expression Levels During CNS Development and Injury , 1998, Pacific Symposium on Biocomputing.
[79] C. Huttenhower,et al. Dynamics of metatranscription in the inflammatory bowel disease gut microbiome , 2018, Nature Microbiology.
[80] R. Knight,et al. The Human Microbiome Project , 2007, Nature.
[81] B. Snel,et al. Comparative assessment of large-scale data sets of protein–protein interactions , 2002, Nature.
[82] George M. Church,et al. Aligning gene expression time series with time warping algorithms , 2001, Bioinform..
[83] Michael Luby,et al. Approximating Probabilistic Inference in Bayesian Belief Networks is NP-Hard , 1993, Artif. Intell..
[84] Bartek Wilczynski,et al. BNFinder: exact and efficient method for learning Bayesian networks , 2008, Bioinform..
[85] Dennis Vitkup,et al. Quantifying spatiotemporal variability and noise in absolute microbiota abundances using replicate sampling , 2019, Nature Methods.
[86] Radu Marculescu,et al. Inferring Microbial Interactions from Metagenomic Time-series Using Prior Biological Knowledge , 2017, BCB.
[87] Karsten Zengler,et al. The challenges of integrating multi-omic data sets. , 2010, Nature chemical biology.
[88] M. Blaser,et al. The human microbiome: at the interface of health and disease , 2012, Nature Reviews Genetics.
[89] Jennifer M. Fettweis,et al. The Integrative Human Microbiome Project , 2019, Nature.
[90] Hans-Werner Mewes,et al. CORUM: the comprehensive resource of mammalian protein complexes , 2007, Nucleic Acids Res..
[91] James R. Foulds,et al. Learning accurate representations of microbe-metabolite interactions , 2019, Nature Methods.
[92] Hiroyuki Kubota,et al. Trans-Omics: How To Reconstruct Biochemical Networks Across Multiple 'Omic' Layers. , 2016, Trends in biotechnology.
[93] M A Krohn,et al. Reliability of diagnosing bacterial vaginosis is improved by a standardized method of gram stain interpretation , 1991, Journal of clinical microbiology.
[94] Christine L. Sun,et al. Temporal and spatial variation of the human microbiota during pregnancy , 2015, Proceedings of the National Academy of Sciences.
[95] Cathy H. Wu,et al. UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..
[96] Tommi S. Jaakkola,et al. Continuous Representations of Time-Series Gene Expression Data , 2003, J. Comput. Biol..
[97] David J. Beale,et al. Beyond Metabolomics: A Review of Multi-Omics-Based Approaches , 2016 .
[98] P. Gajer,et al. Vaginal microbiome of reproductive-age women , 2010, Proceedings of the National Academy of Sciences.
[99] L. T. Angenent,et al. Succession of microbial consortia in the developing infant gut microbiome , 2010, Proceedings of the National Academy of Sciences.
[100] Dan S. Tawfik. Messy biology and the origins of evolutionary innovations. , 2010, Nature chemical biology.
[101] Gunnar Rätsch,et al. Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota , 2013, PLoS Comput. Biol..
[102] Wibke Busch,et al. Prospects and challenges of multi-omics data integration in toxicology , 2020, Archives of Toxicology.
[103] J. Handelsman,et al. Metagenomics: genomic analysis of microbial communities. , 2004, Annual review of genetics.
[104] M. Vaneechoutte,et al. Lactobacillus iners: Friend or Foe? , 2017, Trends in microbiology.
[105] Nir Friedman,et al. Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm , 1999, UAI.
[106] Arun K. Ramani,et al. How complete are current yeast and human protein-interaction networks? , 2006, Genome Biology.
[107] Charlotte M. Deane,et al. What Evidence Is There for the Homology of Protein-Protein Interactions? , 2012, PLoS Comput. Biol..
[108] C. Huttenhower,et al. Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences , 2019, Nature Communications.
[109] Tomi Silander,et al. On Sensitivity of the MAP Bayesian Network Structure to the Equivalent Sample Size Parameter , 2007, UAI.
[110] B. Holloway. Genetic recombination in Pseudomonas aeruginosa. , 1955, Journal of general microbiology.
[111] P. Turnbaugh,et al. An Invitation to the Marriage of Metagenomics and Metabolomics , 2008, Cell.
[112] Geoffrey Zweig,et al. Speech Recognition with Dynamic Bayesian Networks , 1998, AAAI/IAAI.
[113] E. Castro-Nallar,et al. Integrating microbial and host transcriptomics to characterize asthma-associated microbial communities , 2015, BMC Medical Genomics.
[114] Eddy J. Bautista,et al. Longitudinal multi-omics of host–microbe dynamics in prediabetes , 2019, Nature.
[115] Giri Narasimhan,et al. Dynamic interaction network inference from longitudinal microbiome data , 2018 .
[116] S. Abbott,et al. 16S rRNA Gene Sequencing for Bacterial Identification in the Diagnostic Laboratory: Pluses, Perils, and Pitfalls , 2007, Journal of Clinical Microbiology.
[117] Scott T. Weiss,et al. Longitudinal Prediction of the Infant Gut Microbiome with Dynamic Bayesian Networks , 2016, Scientific Reports.
[118] William Stafford Noble,et al. Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra. , 2016, Journal of proteome research.
[119] Karsten Zengler,et al. A Novel Sparse Compositional Technique Reveals Microbial Perturbations , 2019, mSystems.
[120] I. Simon,et al. Studying and modelling dynamic biological processes using time-series gene expression data , 2012, Nature Reviews Genetics.