Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning
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[1] S. Gharbia,et al. Evaluation of Genomic Typing Methods in the Salmonella Reference Laboratory in Public Health, England, 2012–2020 , 2023, Pathogens.
[2] P. Ashton,et al. Global diversity and antimicrobial resistance of typhoid fever pathogens: insights from 13,000 Salmonella Typhi genomes , 2022, medRxiv.
[3] C. Jenkins,et al. Two Outbreaks of Foodborne Gastrointestinal Infection Linked to Consumption of Imported Melons, United Kingdom, March to August 2021. , 2022, Journal of food protection.
[4] Xiangyu Deng,et al. Global spread of Salmonella Enteritidis via centralized sourcing and international trade of poultry breeding stocks , 2021, Nature Communications.
[5] Y. Somorin,et al. Salmonella is the most common foodborne pathogen in African food exports to the European Union: Analysis of the Rapid Alert System for Food and Feed (1999–2019) , 2021 .
[6] Daniel J. Wilson,et al. Machine learning to predict the source of campylobacteriosis using whole genome data , 2021, bioRxiv.
[7] Juno Thomas,et al. Whole-genome sequencing to investigate two concurrent outbreaks of Salmonella Enteritidis in South Africa, 2018. , 2020, Journal of medical microbiology.
[8] P. Njage,et al. Application of Whole‐Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium , 2020, Risk analysis : an official publication of the Society for Risk Analysis.
[9] Nadejda Lupolova,et al. A guide to machine learning for bacterial host attribution using genome sequence data , 2019, Microbial genomics.
[10] S. Nair,et al. The Transformation of Reference Microbiology Methods and Surveillance for Salmonella With the Use of Whole Genome Sequencing in England and Wales , 2019, Front. Public Health.
[11] Páll Melsted,et al. Bifrost: highly parallel construction and indexing of colored and compacted de Bruijn graphs , 2019, Genome Biology.
[12] T. Dallman,et al. An international outbreak of Salmonella enterica serotype Enteritidis linked to eggs from Poland: a microbiological and epidemiological study. , 2019, The Lancet. Infectious diseases.
[13] J. McLauchlin,et al. Public health risks associated with Salmonella contamination of imported edible betel leaves: Analysis of results from England, 2011-2017. , 2019, International journal of food microbiology.
[14] T. Dallman,et al. Impact of whole genome sequencing on the investigation of food-borne outbreaks of Shiga toxin-producing Escherichia coli serogroup O157:H7, England, 2013 to 2017 , 2019, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.
[15] Beau B. Bruce,et al. Zoonotic Source Attribution of Salmonella enterica Serotype Typhimurium Using Genomic Surveillance Data, United States , 2019, Emerging infectious diseases.
[16] Vincent Lacroix,et al. A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events , 2018, bioRxiv.
[17] Moez Sanaa,et al. Source Attribution of Foodborne Diseases: Potentialities, Hurdles, and Future Expectations , 2018, Front. Microbiol..
[18] S. Octavia,et al. Retrospective genome-wide comparisons of Salmonella enterica serovar Enteritidis from suspected outbreaks in Singapore. , 2018, Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases.
[19] Padmini Ramachandran,et al. Genomics of foodborne pathogens for microbial food safety. , 2018, Current opinion in biotechnology.
[20] K. Nagy,et al. The European Union summary report on trends and sources of zoonoses, zoonotic agents and food‐borne outbreaks in 2016 , 2017, EFSA journal. European Food Safety Authority.
[21] N. Wheeler,et al. Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica , 2017, bioRxiv.
[22] T. Dallman,et al. Patchy promiscuity: machine learning applied to predict the host specificity of Salmonella enterica and Escherichia coli , 2017, Microbial genomics.
[23] Richard Myers,et al. SnapperDB: A database solution for routine sequencing analysis of bacterial isolates , 2017, bioRxiv.
[24] S. Mutschall,et al. Source attribution of human campylobacteriosis at the point of exposure by combining comparative exposure assessment and subtype comparison based on comparative genomic fingerprinting , 2017, PloS one.
[25] J. Parkhill,et al. Population genetic structuring of methicillin-resistant Staphylococcus aureus clone EMRSA-15 within UK reflects patient referral patterns , 2017, Microbial genomics.
[26] L. Gould,et al. Outbreaks of Disease Associated with Food Imported into the United States, 1996–2014 , 2017, Emerging infectious diseases.
[27] Nigel French,et al. sourceR: Classification and source attribution of infectious agents among heterogeneous populations , 2017, PLoS Comput. Biol..
[28] Khalil Abudahab,et al. Microreact: visualizing and sharing data for genomic epidemiology and phylogeography , 2016, Microbial genomics.
[29] T Jombart,et al. Prospective use of whole genome sequencing (WGS) detected a multi-country outbreak of Salmonella Enteritidis , 2016, Epidemiology and Infection.
[30] J. Bono,et al. Short-term evolution of Shiga toxin-producing Escherichia coli O157:H7 between two food-borne outbreaks , 2016, Microbial genomics.
[31] Thibaut Jombart,et al. Phylogenetic structure of European Salmonella Enteritidis outbreak correlates with national and international egg distribution network , 2016, Microbial genomics.
[32] Frank Neumann,et al. Proceedings of the Genetic and Evolutionary Computation Conference 2016 , 2016, GECCO 2016.
[33] S. Nair,et al. Distinct Salmonella Enteritidis lineages associated with enterocolitis in high-income settings and invasive disease in low-income settings , 2016, Nature Genetics.
[34] Eric D. Ebel,et al. Comparing Characteristics of Sporadic and Outbreak-Associated Foodborne Illnesses, United States, 2004–2011 , 2016, Emerging infectious diseases.
[35] Paul Medvedev,et al. Compacting de Bruijn graphs from sequencing data quickly and in low memory , 2016, Bioinform..
[36] Claire Jenkins,et al. Identification of Salmonella for public health surveillance using whole genome sequencing , 2016, PeerJ.
[37] Randal S. Olson,et al. Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science , 2016, GECCO.
[38] Simon R. Harris,et al. SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments , 2016, bioRxiv.
[39] A. von Haeseler,et al. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies , 2014, Molecular biology and evolution.
[40] Björn Usadel,et al. Trimmomatic: a flexible trimmer for Illumina sequence data , 2014, Bioinform..
[41] Laura C Rodrigues,et al. Longitudinal study of infectious intestinal disease in the UK (IID2 study): incidence in the community and presenting to general practice , 2011, Gut.
[42] Tine Hald,et al. Attributing the human disease burden of foodborne infections to specific sources. , 2009, Foodborne pathogens and disease.
[43] Georgios S. Vernikos,et al. Comparative genome analysis of Salmonella Enteritidis PT4 and Salmonella Gallinarum 287/91 provides insights into evolutionary and host adaptation pathways. , 2008, Genome research.
[44] Daniel J. Wilson,et al. Tracing the Source of Campylobacteriosis , 2008, PLoS genetics.
[45] P. Donnelly,et al. Inference of population structure using multilocus genotype data. , 2000, Genetics.
[46] Alex A. Freitas,et al. A survey of hierarchical classification across different application domains , 2010, Data Mining and Knowledge Discovery.
[47] Stan Matwin,et al. Functional Annotation of Genes Using Hierarchical Text Categorization , 2005 .
[48] B. Spratt,et al. Recombination and the population structures of bacterial pathogens. , 2001, Annual review of microbiology.