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Knowledge Driven Approaches and Machine Learning Improve the Identification of Clinically Relevant Somatic Mutations in Cancer Genomics
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
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:553299