Gotowa bibliografia na temat „Microbial association networks”
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Artykuły w czasopismach na temat "Microbial association networks"
Lo, Chieh, i Radu Marculescu. "MPLasso: Inferring microbial association networks using prior microbial knowledge". PLOS Computational Biology 13, nr 12 (27.12.2017): e1005915. http://dx.doi.org/10.1371/journal.pcbi.1005915.
Pełny tekst źródłaRocha-Viggiano, Ana K., Saray Aranda-Romo, Mariana Salgado-Bustamante i Cesaré Ovando-Vázquez. "Meconium Microbiota Composition and Association with Birth Delivery Mode". Advanced Gut & Microbiome Research 2022 (7.11.2022): 1–18. http://dx.doi.org/10.1155/2022/6077912.
Pełny tekst źródłaCentler, Florian, Sarah Günnigmann, Ingo Fetzer i Annelie Wendeberg. "Keystone Species and Modularity in Microbial Hydrocarbon Degradation Uncovered by Network Analysis and Association Rule Mining". Microorganisms 8, nr 2 (30.01.2020): 190. http://dx.doi.org/10.3390/microorganisms8020190.
Pełny tekst źródłaAi, Dongmei, Hongfei Pan, Xiaoxin Li, Min Wu i Li C. Xia. "Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls". PeerJ 7 (29.07.2019): e7315. http://dx.doi.org/10.7717/peerj.7315.
Pełny tekst źródłaFaust, Karoline, i Jeroen Raes. "CoNet app: inference of biological association networks using Cytoscape". F1000Research 5 (27.06.2016): 1519. http://dx.doi.org/10.12688/f1000research.9050.1.
Pełny tekst źródłaFaust, Karoline, i Jeroen Raes. "CoNet app: inference of biological association networks using Cytoscape". F1000Research 5 (14.10.2016): 1519. http://dx.doi.org/10.12688/f1000research.9050.2.
Pełny tekst źródłaNagpal, Sunil, Rashmi Singh, Deepak Yadav i Sharmila S. Mande. "MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks". Nucleic Acids Research 48, W1 (27.04.2020): W572—W579. http://dx.doi.org/10.1093/nar/gkaa254.
Pełny tekst źródłaLiu, Fei, Shao-Wu Zhang, Ze-Gang Wei, Wei Chen i Chen Zhou. "Mining Seasonal Marine Microbial Pattern with Greedy Heuristic Clustering and Symmetrical Nonnegative Matrix Factorization". BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/189590.
Pełny tekst źródłaPoudel, R., A. Jumpponen, D. C. Schlatter, T. C. Paulitz, B. B. McSpadden Gardener, L. L. Kinkel i K. A. Garrett. "Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management". Phytopathology® 106, nr 10 (październik 2016): 1083–96. http://dx.doi.org/10.1094/phyto-02-16-0058-fi.
Pełny tekst źródłaAvila-Jimenez, Maria-Luisa, Gavin Burns, Zhili He, Jizhong Zhou, Andrew Hodson, Jose-Luis Avila-Jimenez i David Pearce. "Functional Associations and Resilience in Microbial Communities". Microorganisms 8, nr 6 (24.06.2020): 951. http://dx.doi.org/10.3390/microorganisms8060951.
Pełny tekst źródłaRozprawy doktorskie na temat "Microbial association networks"
Benoiston, Anne-Sophie. "Méta-omique et méta-données environnementales : vers une nouvelle compréhension de la pompe à carbone biologique". Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS182.pdf.
Pełny tekst źródłaThe biological carbon pump encompasses a series of processes including the primary production of organic matter in the surface ocean, its export to deeper waters and its remineralization. The common highlighted actors are diatoms because of their contribution to primary production and carbon export and copepods for their production of fecal pellets. However, the biological pump is the result of complex interactions among organisms rather than their independent actions. Besides, although size distribution and mineral composition of phytoplankton in surface was shown to significantly influence the strength of carbon export, it is unknown whether meta-omic data can efficiently predict the processes of the biological carbon pump. In this thesis, I first propose to revisit the study of the biological carbon pump in the oligotrophic ocean by defining biogeochemical states of the ocean based on the relative contribution of primary production, carbon export and flux attenuation in Tara Oceans sampling stations. The analysis of the states in terms of microbial composition and interactions inferred from metabarcoding data revealed that variation in associations rather than lineages presence seems to drive the states of the biological carbon pump. Then, by using meta-omics and environmental parameters from the Tara Oceans expeditions, I propose the first study trying to predict biogeochemical states from biological abundances derived from environmental DNA, with the goal of providing a list of biomarkers
Części książek na temat "Microbial association networks"
Saikia, Shyamalima, Minakshi Puzari i Pankaj Chetia. "System Biology and Livestock Gut Microbiome". W Systems Biology, Bioinformatics and Livestock Science, 96–128. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815165616123010010.
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