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Bioinformatics Software Systems for ...
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Liu, Yu.
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Bioinformatics Software Systems for the Study of Circadian Rhythms.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Bioinformatics Software Systems for the Study of Circadian Rhythms./
作者:
Liu, Yu.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:
140 p.
附註:
Source: Dissertation Abstracts International, Volume: 80-05(E), Section: B.
Contained By:
Dissertation Abstracts International80-05B(E).
標題:
Bioinformatics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10839223
ISBN:
9780438744684
Bioinformatics Software Systems for the Study of Circadian Rhythms.
Liu, Yu.
Bioinformatics Software Systems for the Study of Circadian Rhythms.
- Ann Arbor : ProQuest Dissertations & Theses, 2018 - 140 p.
Source: Dissertation Abstracts International, Volume: 80-05(E), Section: B.
Thesis (Ph.D.)--University of California, Irvine, 2018.
The study of circadian rhythms increasingly relies on high throughput circadian omic data. Processing, annotating and analyzing these data require an efficient and powerful bioinformatics software system. To this end, we have implemented such a system. Major components of this system include genome-wide binding sites predictors, statistical and machine learning pipelines, functional enrichment pipelines, visualization tools and a data management server. Using this system, we have produced high impact publications, a circadian web portal (CircadiOmics, http://circadiomics.ics.uci.edu/), and advanced our understanding of the organization of the circadian transcriptome.
ISBN: 9780438744684Subjects--Topical Terms:
553671
Bioinformatics.
Bioinformatics Software Systems for the Study of Circadian Rhythms.
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The study of circadian rhythms increasingly relies on high throughput circadian omic data. Processing, annotating and analyzing these data require an efficient and powerful bioinformatics software system. To this end, we have implemented such a system. Major components of this system include genome-wide binding sites predictors, statistical and machine learning pipelines, functional enrichment pipelines, visualization tools and a data management server. Using this system, we have produced high impact publications, a circadian web portal (CircadiOmics, http://circadiomics.ics.uci.edu/), and advanced our understanding of the organization of the circadian transcriptome.
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