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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 | E-Book |
|---|---|
| 개인저자 | Ainscough, Benjamin John. |
| 단체저자명 | Washington University in St. Louis. Biology & Biomedical Sciences (Human & Statistical Genetics). |
| 서명/저자사항 | Knowledge Driven Approaches and Machine Learning Improve the Identification of Clinically Relevant Somatic Mutations in Cancer Genomics. |
| 발행사항 | [S.l.] : Washington University in St. Louis., 2017 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2017 |
| 형태사항 | 181 p. |
| 소장본 주기 | School code: 0252. |
| ISBN | 9780355555370 |
| 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 |
| 기본자료 저록 | Dissertation Abstracts International79-05B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14996796 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00024444 | DP 574 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출불가(별치) |
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