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Knowledge Driven Approaches and Machine Learning Improve the Identification of Clinically Relevant Somatic Mutations in Cancer Genomics
内容资讯
Knowledge Driven Approaches and Machine Learning Improve the Identification of Clinically Relevant Somatic Mutations in Cancer Genomics
자료유형  
 학위논문
Control Number  
0014996796
International Standard Book Number  
9780355555370
Dewey Decimal Classification Number  
574
Main Entry-Personal Name  
Ainscough, Benjamin John.
Publication, Distribution, etc. (Imprint  
[Sl] : Washington University in St Louis, 2017
Publication, Distribution, etc. (Imprint  
Ann Arbor : ProQuest Dissertations & Theses, 2017
Physical Description  
181 p
General Note  
Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
General Note  
Advisers: Obi L. Griffith
Dissertation Note  
Thesis (Ph.D.)--Washington University in St. Louis, 2017.
Restrictions on Access Note  
This item is not available from ProQuest Dissertations & Theses.
Summary, Etc.  
요약For cancer genomics to fully expand its utility from research discovery to clinical adoption, somatic variant detection pipelines must be optimized and standardized to ensure identification of clinically relevant mutations and to reduce laboriou
Subject Added Entry-Topical Term  
Bioinformatics
Subject Added Entry-Topical Term  
Artificial intelligence
Subject Added Entry-Topical Term  
Genetics
Added Entry-Corporate Name  
Washington University in St. Louis Biology & Biomedical Sciences (Human & Statistical Genetics)
Host Item Entry  
Dissertation Abstracts International. 79-05B(E).
Host Item Entry  
Dissertation Abstract International
Electronic Location and Access  
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Control Number  
joongbu:553299
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