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Knowledge Driven Approaches and Machine Learning Improve the Identification of Clinically Relevant Somatic Mutations in Cancer Genomics
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Knowledge Driven Approaches and Machine Learning Improve the Identification of Clinically Relevant Somatic Mutations in Cancer Genomics. |
| 개인저자 | Ainscough, Benjamin John. |
| 단체저자명 | Washington University in St. Louis. Biology & Biomedical Sciences (Human & Statistical Genetics). |
| 발행사항 | [S.l.]: Washington University in St. Louis., 2017. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2017. |
| 형태사항 | 181 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-05B(E). Dissertation Abstract International |
| ISBN | 9780355555370 |
| 학위논문주기 | Thesis (Ph.D.)--Washington University in St. Louis, 2017. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
Advisers: Obi L. Griffith |
| 이용제한사항 | This item is not available from ProQuest Dissertations & Theses. |
| 요약 | For 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 |
| 일반주제명 | Bioinformatics. Artificial intelligence. Genetics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
