東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

New clustering and feature selection...

Case Western Reserve University.

FindBook

Google Book

Amazon

博客來

New clustering and feature selection procedures with applications to gene microarray data.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	New clustering and feature selection procedures with applications to gene microarray data./
作者:	Xu, Yaomin.
面頁冊數:	219 p.
附註:	Adviser: Jiayang Sun.
Contained By:	Dissertation Abstracts International68-10B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3286195
ISBN:	9780549290957

New clustering and feature selection procedures with applications to gene microarray data.
Xu, Yaomin.

New clustering and feature selection procedures with applications to gene microarray data. - 219 p.

Adviser: Jiayang Sun.

Thesis (Ph.D.)--Case Western Reserve University, 2008.

Key words: Bioinformatics, coherence index, data mining, feature selection, gene expression pathway, gene profiling, informative gene, microarray data, profile cluster analysis, partitioning, regulatory network, statistical pattern recognition.

ISBN: 9780549290957Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

New clustering and feature selection procedures with applications to gene microarray data.
LDR:03403nam 2200349 a 45 001 856571
005 20100709
008 100709s2008 ||||||||||||||||| ||eng d
020 $a 9780549290957
035 $a (UMI)AAI3286195
035 $a AAI3286195
040 $a UMI $c UMI
100 1 $a Xu, Yaomin. $3 1023394
245 1 0 $a New clustering and feature selection procedures with applications to gene microarray data.
300 $a 219 p.
500 $a Adviser: Jiayang Sun.
500 $a Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6743.
502 $a Thesis (Ph.D.)--Case Western Reserve University, 2008.
520 $a Key words: Bioinformatics, coherence index, data mining, feature selection, gene expression pathway, gene profiling, informative gene, microarray data, profile cluster analysis, partitioning, regulatory network, statistical pattern recognition.
520 $a Statistical data mining is one of the most active research areas. In this thesis we develop two new data mining procedures and explore their applications to genetic data.
520 $a The ideas in our two procedures can be generalized and applied to other data mining tasks. This thesis concludes with discussion on connections between two methods and the related future research.
520 $a The first procedure is called PfCluster---Profile Cluster Analysis. It is a clustering method designed for profiled genetic data. The PfCluster is efficient and flexible in uncovering clusters determined by a new class of biologically meaningful distance metrics. A new internal quality measure of clusters, coherence index, is developed to find coherent clusters. An efficient mechanism for choosing the threshold of coherent clusters is also derived and implemented. The threshold is based on the first and second order approximations to the true threshold under a null distribution for parallel clusters.
520 $a The PfCluster has been applied to simulated data and two real data examples: a biomarker LOH dataset and a microarray gene expression dataset. PfCluster is competitive to the correlation-based clustering procedures. The second procedure is called RPselection---Resampling based partitioning selection. It is a feature selection algorithm designed for microarray studies. It selects a subset of genes that maximizes a fitness score. The fitness score measures the relevance between the partition labels from a clustering result and an external class label derived from the clinical outcomes. The score is computed using a resampling procedure. The RPselection algorithm has been applied to simulated data and a real uveal melanoma gene expression data. RPselection outperforms gene-by-gene test-based feature selection procedures.
520 $a Software development is an integral part of modern statistical research. Two software packages, pfclust and rpselect, are developed in this thesis based on our PfCluster method and RPselection algorithm. Packages pfclust and rpselect are implemented based on R object-oriented programming framework, and they can be easily customized and extended by users.
590 $a School code: 0042.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
690 $a 0308
690 $a 0463
690 $a 0715
710 2 $a Case Western Reserve University. $3 1017714
773 0 $t Dissertation Abstracts International $g 68-10B.
790 $a 0042
790 1 0 $a Sun, Jiayang, $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3286195