東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Epidemiological Inference from Pathogen Genome Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Epidemiological Inference from Pathogen Genome Data./
作者:	Bandoy, D.J. Darwin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	130 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
標題:	Epidemiology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28968724
ISBN:	9798438736134

Epidemiological Inference from Pathogen Genome Data.
Bandoy, D.J. Darwin.

Epidemiological Inference from Pathogen Genome Data. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 130 p.

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--University of California, Davis, 2022.

This item must not be sold to any third party vendors.

The use of whole genome sequencing in infectious disease diagnostics generated an unprecedented amount and resolution of information. Large-scale sequencing of pathogens requires scalable methods in species identification, outbreak clustering, virulence phenotyping, antimicrobial resistance profiling, and epidemic modeling. This dissertation presents a new approach in defining species membership using a pangenome framework explicitly applied to the whole genome sequences of the genus Hungatella which effectively identified a misclassified reference strain. Genomic epidynamics is a phylogenetic free approach in epidemiological inference, particularly the disease transmission parameter reproductive number (R). This approach offers a scalable process in elucidating heterogeneous transmission of genomic variants of SARS-CoV-2. Genomic epidynamics bridges pathogen population genomics and epidemic modeling. A genome-first approach to antimicrobial resistance definition combines automated machine learning rank resistance genes and phenotypic data thru genomic MICs. This approach was applied to a multidrug-resistant serotype of Salmonella enterica subsp. enterica serovar Dublin (S. Dublin). Machine learning-based approach to genome-wide association study revealed allelic variants of porA in Campylobacter jejuni leading to an abortive phenotype when the organism is invasive from the gut and resides in the reproductive system.

ISBN: 9798438736134Subjects--Topical Terms:

568544
Epidemiology.
Subjects--Index Terms:

Antimicrobial resistance

Epidemiological Inference from Pathogen Genome Data.
LDR:02607nmm a2200385 4500 001 2348934
005 20220920134625.5
008 241004s2022 ||||||||||||||||| ||eng d
020 $a 9798438736134
035 $a (MiAaPQ)AAI28968724
035 $a AAI28968724
040 $a MiAaPQ $c MiAaPQ
100 1 $a Bandoy, D.J. Darwin. $3 3688317
245 1 0 $a Epidemiological Inference from Pathogen Genome Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 130 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
500 $a Advisor: Weimer, Bart C.
502 $a Thesis (Ph.D.)--University of California, Davis, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a The use of whole genome sequencing in infectious disease diagnostics generated an unprecedented amount and resolution of information. Large-scale sequencing of pathogens requires scalable methods in species identification, outbreak clustering, virulence phenotyping, antimicrobial resistance profiling, and epidemic modeling. This dissertation presents a new approach in defining species membership using a pangenome framework explicitly applied to the whole genome sequences of the genus Hungatella which effectively identified a misclassified reference strain. Genomic epidynamics is a phylogenetic free approach in epidemiological inference, particularly the disease transmission parameter reproductive number (R). This approach offers a scalable process in elucidating heterogeneous transmission of genomic variants of SARS-CoV-2. Genomic epidynamics bridges pathogen population genomics and epidemic modeling. A genome-first approach to antimicrobial resistance definition combines automated machine learning rank resistance genes and phenotypic data thru genomic MICs. This approach was applied to a multidrug-resistant serotype of Salmonella enterica subsp. enterica serovar Dublin (S. Dublin). Machine learning-based approach to genome-wide association study revealed allelic variants of porA in Campylobacter jejuni leading to an abortive phenotype when the organism is invasive from the gut and resides in the reproductive system.
590 $a School code: 0029.
650 4 $a Epidemiology. $3 568544
650 4 $a Genetics. $3 530508
650 4 $a Bioinformatics. $3 553671
653 $a Antimicrobial resistance
653 $a COVID-19
653 $a Epidemic model
653 $a Genomics
653 $a Reproductive number
653 $a Salmonella
690 $a 0766
690 $a 0369
690 $a 0715
710 2 $a University of California, Davis. $b Integrative Pathobiology. $3 3350246
773 0 $t Dissertations Abstracts International $g 83-11B.
790 $a 0029
791 $a Ph.D.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28968724