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Statistical Learning Methods for Big Biomedical Data

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개인저자Li, Ziyi.
단체저자명Emory University. Biostatistics.
서명/저자사항Statistical Learning Methods for Big Biomedical Data.
발행사항[S.l.] : Emory University., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항143 p.
소장본 주기School code: 0665.
ISBN9780438238848
일반주기 Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Qi Long.
요약The 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
요약Principal 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
요약Electronic 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
요약Biclustering 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
요약For 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
일반주제명Biostatistics.
Bioinformatics.
언어영어
기본자료 저록Dissertation Abstracts International79-12B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T15001220

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