| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000330117 |
| 005 | | 20241024090714 |
| 008 | | 181129s2017 ||| | | | eng d |
| 020 | |
▼a 9780355555370 |
| 035 | |
▼a (MiAaPQ)AAI10742563 |
| 035 | |
▼a (MiAaPQ)wustl:12406 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 574 |
| 100 | 1 |
▼a Ainscough, Benjamin John.
▼0 (orcid)0000-0001-8340-514X |
| 245 | 10 |
▼a Knowledge Driven Approaches and Machine Learning Improve the Identification of Clinically Relevant Somatic Mutations in Cancer Genomics. |
| 260 | |
▼a [S.l.] :
▼b Washington University in St. Louis.,
▼c 2017 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2017 |
| 300 | |
▼a 181 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B. |
| 500 | |
▼a Advisers: Obi L. Griffith |
| 502 | 1 |
▼a Thesis (Ph.D.)--Washington University in St. Louis, 2017. |
| 506 | |
▼a This item is not available from ProQuest Dissertations & Theses. |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0252. |
| 650 | 4 |
▼a Bioinformatics. |
| 650 | 4 |
▼a Artificial intelligence. |
| 650 | 4 |
▼a Genetics. |
| 690 | |
▼a 0715 |
| 690 | |
▼a 0800 |
| 690 | |
▼a 0369 |
| 710 | 20 |
▼a Washington University in St. Louis.
▼b Biology & Biomedical Sciences (Human & Statistical Genetics). |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-05B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0252 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996796
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |