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

Improving the performance and precis...

University of Maryland, College Park., Computer Science.

FindBook

Google Book

Amazon

博客來

Improving the performance and precision of bioinformatic algorithms.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Improving the performance and precision of bioinformatic algorithms./
作者:	Wu, Xue.
面頁冊數:	201 p.
附註:	Adviser: Chau-Wen Tseng.
Contained By:	Dissertation Abstracts International69-08B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3324936
ISBN:	9780549782094

Improving the performance and precision of bioinformatic algorithms.
Wu, Xue.

Improving the performance and precision of bioinformatic algorithms. - 201 p.

Adviser: Chau-Wen Tseng.

Thesis (Ph.D.)--University of Maryland, College Park, 2008.

Recent advances in biotechnology have enabled scientists to generate and collect huge amounts of biological experimental data. Software tools for analyzing both genomic (DNA) and proteomic (protein) data with high speed and accuracy have thus become very important in modern biological research. This thesis presents several techniques for improving the performance and precision of bioinformatics algorithms used by biologists.

ISBN: 9780549782094Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Improving the performance and precision of bioinformatic algorithms.
LDR:03381nam 2200325 a 45 001 854250
005 20100702
008 100702s2008 ||||||||||||||||| ||eng d
020 $a 9780549782094
035 $a (UMI)AAI3324936
035 $a AAI3324936
040 $a UMI $c UMI
100 1 $a Wu, Xue. $3 1020572
245 1 0 $a Improving the performance and precision of bioinformatic algorithms.
300 $a 201 p.
500 $a Adviser: Chau-Wen Tseng.
500 $a Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4867.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2008.
520 $a Recent advances in biotechnology have enabled scientists to generate and collect huge amounts of biological experimental data. Software tools for analyzing both genomic (DNA) and proteomic (protein) data with high speed and accuracy have thus become very important in modern biological research. This thesis presents several techniques for improving the performance and precision of bioinformatics algorithms used by biologists.
520 $a Improvements in both the speed and cost of automated DNA sequencers have allowed scientists to sequence the DNA of an increasing number of organisms. One way biologists can take advantage of this genomic DNA data is to use it in conjunction with expressed sequence tag (EST) and cDNA sequences to find genes and their splice sites. This thesis describes ESTmapper, a tool designed to use an eager write-only top-down (WOTD) suffix tree to efficiently align DNA sequences against known genomes. Experimental results show that ESTmapper can be much faster than previous techniques for aligning and clustering DNA sequences, and produces alignments of comparable or better quality.
520 $a Peptide identification by tandem mass spectrometry (MS/MS) is becoming the dominant high-throughput proteomics workflow for protein characterization in complex samples. Biologists currently rely on protein database search engines to identify peptides producing experimentally observed mass spectra. This thesis describes two approaches for improving peptide identification precision using statistical machine learning.
520 $a HMMatch (HMM MS/MS Match) is a hidden Markov model approach to spectral matching, in which many examples of a peptide fragmentation spectrum are summarized in a generative probabilistic model that captures the consensus and variation of each peak's intensity. Experimental results show that HMMatch can identify many peptides missed by traditional spectral matching and search engines.
520 $a PepArML (Peptide Identification Arbiter by Machine Learning) is a machine learning based framework for improving the precision of peptide identification. It uses classification algorithms to effectively utilize spectra features and scores from multiple search engines in a single model-free framework that can be trained in an unsupervised manner. Experimental results show that PepArML can improve the sensitivity of peptide identification for several synthetic protein mixtures compared with individual search engines.
590 $a School code: 0117.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0984
710 2 $a University of Maryland, College Park. $b Computer Science. $3 1018451
773 0 $t Dissertation Abstracts International $g 69-08B.
790 $a 0117
790 1 0 $a Tseng, Chau-Wen, $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3324936