Disentangling Interactions in the Microbiome: A Network Perspective
Tài liệu tham khảo
Hooper, 2012, Interactions between the microbiota and the immune system, Science, 336, 1268, 10.1126/science.1223490
Thaiss, 2016, The microbiome and innate immunity, Nature, 535, 65, 10.1038/nature18847
Hacquard, 2015, Microbiota and host nutrition across plant and animal kingdoms, Cell Host Microbe, 17, 603, 10.1016/j.chom.2015.04.009
Pop, 2014, Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition, Genome Biol., 15, R76, 10.1186/gb-2014-15-6-r76
Gülden, 2015, The gut microbiota and Type 1 Diabetes, Clin. Immunol., 159, 143, 10.1016/j.clim.2015.05.013
Yu, 2015, Gut microbiota and colorectal cancer, Gastrointest. Tumors, 2, 26, 10.1159/000380892
Morgan, 2012, Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment, Genome Biol., 13, R79, 10.1186/gb-2012-13-9-r79
Jalanka-Tuovinen, 2014, Faecal microbiota composition and host–microbe cross-talk following gastroenteritis and in postinfectious irritable bowel syndrome, Gut, 63, 1737, 10.1136/gutjnl-2013-305994
Perry, 2016, Acetate mediates a microbiome–brain–β-cell axis to promote metabolic syndrome, Nature, 534, 213, 10.1038/nature18309
Stecher, 2012, Gut inflammation can boost horizontal gene transfer between pathogenic and commensal Enterobacteriaceae, Proc. Natl. Acad. Sci. U.S.A., 109, 1269, 10.1073/pnas.1113246109
Rodrigues Hoffmann, 2016, The microbiome: the trillions of microorganisms that maintain health and cause disease in humans and companion animals, Vet. Pathol., 53, 10, 10.1177/0300985815595517
Hansen, 2015, The gut microbiome in cardio-metabolic health, Genome Med., 7, 33, 10.1186/s13073-015-0157-z
Rogers, 2013, Interpreting infective microbiota: the importance of an ecological perspective, Trends Microbiol., 21, 271, 10.1016/j.tim.2013.03.004
Passos da Silva, 2014, Bacterial multispecies studies and microbiome analysis of a plant disease, Microbiol. Read. Engl., 160, 556, 10.1099/mic.0.074468-0
Dalton, 2011, An in vivo polymicrobial biofilm wound infection model to study interspecies interactions, PloS One, 6, e27317, 10.1371/journal.pone.0027317
Murray, 2014, Mechanisms of synergy in polymicrobial infections, J. Microbiol. Seoul Korea, 52, 188
Proulx, 2005, Network thinking in ecology and evolution, Trends Ecol. Evol., 20, 345, 10.1016/j.tree.2005.04.004
Faust, 2012, Microbial interactions: from networks to models, Nat. Rev. Microbiol., 10, 538, 10.1038/nrmicro2832
Faust, 2012, Microbial co-occurrence relationships in the human microbiome, PLoS Comput. Biol., 8, e1002606, 10.1371/journal.pcbi.1002606
Lima-Mendez, 2015, Determinants of community structure in the global plankton interactome, Science, 348, 1262073, 10.1126/science.1262073
Arumugam, 2011, Enterotypes of the human gut microbiome, Nature, 473, 174, 10.1038/nature09944
Barberán, 2012, Using network analysis to explore co-occurrence patterns in soil microbial communities, ISME J., 6, 343, 10.1038/ismej.2011.119
Copeland, 2015, Seasonal community succession of the phyllosphere microbiome, Mol. Plant. Microbe Interact., 28, 274, 10.1094/MPMI-10-14-0331-FI
Chen, 2016, A two-part mixed-effects model for analyzing longitudinal microbiome compositional data, Bioinformatics, 32, 2611, 10.1093/bioinformatics/btw308
Weiss, 2016, Correlation detection strategies in microbial data sets vary widely in sensitivity and precision, ISME J., 10, 1669, 10.1038/ismej.2015.235
van den Bergh, 2012, Associations between pathogens in the upper respiratory tract of young children: interplay between viruses and bacteria, PloS One, 7, e47711, 10.1371/journal.pone.0047711
Mitra, 2013, Integrative approaches for finding modular structure in biological networks, Nat. Rev. Genet., 14, 719, 10.1038/nrg3552
Freeman, 1978, Centrality in social networks conceptual clarification, Soc. Netw., 1, 215, 10.1016/0378-8733(78)90021-7
Brandes, 2001, A faster algorithm for betweenness centrality, J. Math. Sociol., 25, 163, 10.1080/0022250X.2001.9990249
Bonacich, 1987, Power and centrality: a family of measures, Am. J. Sociol., 92, 1170, 10.1086/228631
Bauer, 2012, Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: a walk counting approach, EPL Europhys. Lett., 99, 68007, 10.1209/0295-5075/99/68007
Sikic, 2013, Epidemic centrality - is there an underestimated epidemic impact of network peripheral nodes?, Eur. Phys. J. B, 86, 440, 10.1140/epjb/e2013-31025-5
Berry, 2014, Deciphering microbial interactions and detecting keystone species with co-occurrence networks, Microb. Symbioses, 5, 219
Viana, 2013, Accessibility in networks: A useful measure for understanding social insect nest architecture, Chaos Solitons Fractals, 46, 38, 10.1016/j.chaos.2012.11.003
Lawyer, 2014, Understanding the spreading power of all nodes in a network, Sci. Rep., 5, 8665, 10.1038/srep08665
Brin, 1998, The anatomy of a large-scale hypertextual web search engine, 107
Kleinberg, 1999, Authoritative sources in a hyperlinked environment, JACM, 46, 604, 10.1145/324133.324140
Faith, 2013, The long-term stability of the human gut microbiota, Science, 341, 1237439, 10.1126/science.1237439
Cho, 2012, Antibiotics in early life alter the murine colonic microbiome and adiposity, Nature, 488, 621, 10.1038/nature11400
David, 2014, Diet rapidly and reproducibly alters the human gut microbiome, Nature, 505, 559, 10.1038/nature12820
Xia, 2013, Efficient statistical significance approximation for local similarity analysis of high-throughput time series data, Bioinforma. Oxf. Engl., 29, 230, 10.1093/bioinformatics/bts668
Xia, 2011, Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates, BMC Syst. Biol., 5, 1, 10.1186/1752-0509-5-S2-S15
Ruan, 2006, Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors, Bioinforma. Oxf. Engl., 22, 2532, 10.1093/bioinformatics/btl417
Eiler, 2012, Coherent dynamics and association networks among lake bacterioplankton taxa, ISME J., 6, 330, 10.1038/ismej.2011.113
McGeachie, 2016, Longitudinal prediction of the infant gut microbiome with dynamic Bayesian networks, Sci. Rep., 6, 20359, 10.1038/srep20359
Faust, 2015, Metagenomics meets time series analysis: unraveling microbial community dynamics, Curr. Opin. Microbiol., 25, 56, 10.1016/j.mib.2015.04.004
Murray, 2013, Bayesian Gaussian copula factor models for mixed data, J. Am. Stat. Assoc., 108, 656, 10.1080/01621459.2012.762328
Žitnik, 2015, Data fusion by matrix factorization, IEEE Trans. Pattern Anal. Mach. Intell., 37, 41, 10.1109/TPAMI.2014.2343973
Žitnik, 2013, Discovering disease-disease associations by fusing systems-level molecular data, Sci. Rep., 3, 3202, 10.1038/srep03202
Žitnik, 2014, Matrix factorization-based data fusion for drug-induced liver injury prediction, Syst. Biomed., 2, 16, 10.4161/sysb.29072
Žitnik, 2015, Gene network inference by fusing data from diverse distributions, Bioinformatics, 31, i230, 10.1093/bioinformatics/btv258
Erdős, 1960, On the evolution of random graphs, Publ. Math. Inst. Hung. Acad. Sci., 5, 17
Barabási, 1999, Emergence of scaling in random networks, Science, 286, 509, 10.1126/science.286.5439.509
Nafis, 2014, Apoptosis regulatory protein–protein interaction demonstrates hierarchical scale-free fractal network, Brief. Bioinform., 16, 675, 10.1093/bib/bbu036
Teschendorff, 2015, Increased signaling entropy in cancer requires the scale-free property of protein interaction networks, Sci. Rep., 5, 9646, 10.1038/srep09646
Li, 2005, Towards a theory of Scale-free graphs: definition, properties, and implications, Internet Math., 2, 431, 10.1080/15427951.2005.10129111
Watts, 1998, Collective dynamics of “small-world” networks, Nature, 393, 440, 10.1038/30918
Humphries, 2008, Network “small-world-ness”: a quantitative method for determining canonical network equivalence, PLoS One, 3, e2051, 10.1371/journal.pone.0002051
Ma, 2007, An Arabidopsis gene network based on the graphical Gaussian model, Genome Res., 17, 1614, 10.1101/gr.6911207
Wille, 2006, Low-order conditional independence graphs for inferring genetic networks, Stat. Appl. Genet. Mol. Biol., 5, 1, 10.2202/1544-6115.1170
Kurtz, 2015, Sparse and compositionally robust inference of microbial ecological networks, PLoS Comput. Biol., 11, e1004226, 10.1371/journal.pcbi.1004226
Friedman, 2012, Inferring correlation networks from genomic survey data, PLoS Comput. Biol., 8, e1002687, 10.1371/journal.pcbi.1002687
Bell, 2016, A lipid-accumulating alga maintains growth in outdoor, alkaliphilic raceway pond with mixed microbial communities, Microbiotechnology Ecotoxicol. Bioremediation, 6, 1480
Costea, 2014, A fair comparison, Nat. Methods, 11, 10.1038/nmeth.2897
Faust, 2016, CoNet app: inference of biological association networks using Cytoscape, F1000Research, 5, 1519, 10.12688/f1000research.9050.2
Faust, 2015, Cross-biome comparison of microbial association networks, Syst. Microbiol., 6, 1200
Ban, 2015, Investigating microbial co-occurrence patterns based on metagenomic compositional data, Bioinformatics, 31, 3322, 10.1093/bioinformatics/btv364
Fang, 2015, CCLasso: correlation inference for compositional data through Lasso, Bioinformatics, 31, 3172, 10.1093/bioinformatics/btv349
Deng, 2012, Molecular ecological network analyses, BMC Bioinformatics, 13, 113, 10.1186/1471-2105-13-113
Biswas, 2015, Learning microbial interaction networks from metagenomic count data, 32
Girvan, 2002, Community structure in social and biological networks, Proc. Natl. Acad. Sci. U.S.A., 99, 7821, 10.1073/pnas.122653799
Pons, 2005, Computing communities in large networks using random walks, 284
Blondel, 2008, Fast unfolding of communities in large networks, J. Stat. Mech. Theory Exp., 2008, P10008, 10.1088/1742-5468/2008/10/P10008
Van Dongen, 2008, Graph clustering via a discrete uncoupling process, SIAM J. Matrix Anal. Appl., 30, 121, 10.1137/040608635
Hua, 2013, Epigenomic programming contributes to the genomic drift evolution of the F-Box protein superfamily in Arabidopsis, Proc. Natl. Acad. Sci. U.S.A., 110, 16927, 10.1073/pnas.1316009110
Richards, 2014, Phylogenomics and the dynamic genome evolution of the genus Streptococcus, Genome Biol. Evol., 6, 741, 10.1093/gbe/evu048
Lei, 2016, Protein complex identification through Markov clustering with firefly algorithm on dynamic protein–protein interaction networks, Inf. Sci., 329, 303, 10.1016/j.ins.2015.09.028
Wang, 2013, Construction and application of dynamic protein interaction network based on time course gene expression data, PROTEOMICS, 13, 301, 10.1002/pmic.201200277