자료유형 | E-Book |
---|---|
개인저자 | Li, Ziyi. |
단체저자명 | Emory University. Biostatistics. |
서명/저자사항 | Statistical Learning Methods for Big Biomedical Data. |
발행사항 | [S.l.] : Emory University., 2018 |
발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
형태사항 | 143 p. |
소장본 주기 | School code: 0665. |
ISBN | 9780438238848 |
일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Qi Long. |
요약 | The rapid advancement of biological and clinical technologies has generated several distinct types of big biomedical data, including -omics data and electronic health record data. Such data and their distinct features have created challenges in |
요약 | Principal component analysis (PCA) is a popular tool for dimensionality reduction, data mining, and visualization of high dimensional data. It has been recognized that complex biological mechanisms occur through concerted relationships of multip |
요약 | Electronic health record (EHR) data provide promising opportunity to explore personalized treatment regime and to make clinical predictions. Compared with genomics data, EHR data are known for their irregularity and complexity. In addition, anal |
요약 | Biclustering technique can identify local patterns of a data matrix by clustering rows and columns at the same time. Various biclustering methods have been proposed and successfully applied to analyze gene expression data. While existing biclust |
요약 | For future work, we can continue the direction of the first topic and explore the potential extension of sparse PCA combining neural network, or continue the direction of the second topic and replace Word2Vec with recently proposed embedding app |
일반주제명 | Biostatistics. Bioinformatics. |
언어 | 영어 |
기본자료 저록 | Dissertation Abstracts International79-12B(E). Dissertation Abstract International |
대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T15001220 |
인쇄
No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
---|---|---|---|---|---|---|---|---|
1 | WE00026482 | 574 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |