東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Informatics framework for evaluating...

Piccolo, Stephen Richard.

Linked to FindBook

Google Book

Amazon

博客來

Informatics framework for evaluating multivariate prognosis models: Application to human glioblastoma multiforme.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Informatics framework for evaluating multivariate prognosis models: Application to human glioblastoma multiforme./
Author:	Piccolo, Stephen Richard.
Description:	142 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: 1867.
Contained By:	Dissertation Abstracts International72-04B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3440218
ISBN:	9781124470108

Informatics framework for evaluating multivariate prognosis models: Application to human glioblastoma multiforme.
Piccolo, Stephen Richard.

Informatics framework for evaluating multivariate prognosis models: Application to human glioblastoma multiforme. - 142 p.

Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: 1867.

Thesis (Ph.D.)--The University of Utah, 2011.

For decades, researchers have explored the effects of clinical and biomolecular factors on disease outcomes and have identified several candidate prognostic markers. Now, thanks to technological advances, researchers have at their disposal unprecedented quantities of biomolecular data that may add to existing knowledge about prognosis. However, commensurate challenges accompany these advances. For example, sophisticated informatics techniques are necessary to store, retrieve, and analyze large data sets. Additionally, advanced algorithms may be necessary to account for the joint effects of tens, hundreds, or thousands of variables. Moreover, it is essential that analyses evaluating such algorithms be conducted in a systematic and consistent way to ensure validity, repeatability, and comparability across studies. For this study, a novel informatics framework was developed to address these needs. Within this framework, the user can apply existing, general-purpose algorithms that are designed to make multivariate predictions for large, hetergeneous data sets. The framework also contains logic for aggregating evidence across multiple algorithms and data categories via ensemble-learning approaches. In this study, this informatics framework was applied to developing multivariate prognisis models for human glioblastoma multiforme, a highly aggressive form of brain cancer that results in a median survival of only 12-15 months. Data for this study came from The Cancer Genome Atlas, a publicly available repository containing clinical, treatment, histological, and biomolecular variables for hundreds of patients. A variety of variable-selection approaches and multivariate algorithms were applied in a cross-validated design, and the quality of the resulting models was measured using the error rate, area under the receiver operating characteristic curve, and log-rank statistic. Although performance of the algorithms varied substantially across the data categories, some models performed well for all three metrics---particularly models based on age, treatments, and DNA methylation. Also encouragingly, the performance of ensemble-learning methods often approximated the best individual results. As multimodal data sets become more prevalent, analytic approaches that account for multiple data categories and algorithms will be increasingly relevant. This study suggests that such approaches hold promise to guide researchers and clinicians in their quest to improve outcomes for devastating diseases like GBM.

ISBN: 9781124470108Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Informatics framework for evaluating multivariate prognosis models: Application to human glioblastoma multiforme.
LDR:03637nam 2200313 4500 001 1403009
005 20111108080348.5
008 130515s2011 ||||||||||||||||| ||eng d
020 $a 9781124470108
035 $a (UMI)AAI3440218
035 $a AAI3440218
040 $a UMI $c UMI
100 1 $a Piccolo, Stephen Richard. $3 1682241
245 1 0 $a Informatics framework for evaluating multivariate prognosis models: Application to human glioblastoma multiforme.
300 $a 142 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: 1867.
500 $a Adviser: Lewis J. Frey.
502 $a Thesis (Ph.D.)--The University of Utah, 2011.
520 $a For decades, researchers have explored the effects of clinical and biomolecular factors on disease outcomes and have identified several candidate prognostic markers. Now, thanks to technological advances, researchers have at their disposal unprecedented quantities of biomolecular data that may add to existing knowledge about prognosis. However, commensurate challenges accompany these advances. For example, sophisticated informatics techniques are necessary to store, retrieve, and analyze large data sets. Additionally, advanced algorithms may be necessary to account for the joint effects of tens, hundreds, or thousands of variables. Moreover, it is essential that analyses evaluating such algorithms be conducted in a systematic and consistent way to ensure validity, repeatability, and comparability across studies. For this study, a novel informatics framework was developed to address these needs. Within this framework, the user can apply existing, general-purpose algorithms that are designed to make multivariate predictions for large, hetergeneous data sets. The framework also contains logic for aggregating evidence across multiple algorithms and data categories via ensemble-learning approaches. In this study, this informatics framework was applied to developing multivariate prognisis models for human glioblastoma multiforme, a highly aggressive form of brain cancer that results in a median survival of only 12-15 months. Data for this study came from The Cancer Genome Atlas, a publicly available repository containing clinical, treatment, histological, and biomolecular variables for hundreds of patients. A variety of variable-selection approaches and multivariate algorithms were applied in a cross-validated design, and the quality of the resulting models was measured using the error rate, area under the receiver operating characteristic curve, and log-rank statistic. Although performance of the algorithms varied substantially across the data categories, some models performed well for all three metrics---particularly models based on age, treatments, and DNA methylation. Also encouragingly, the performance of ensemble-learning methods often approximated the best individual results. As multimodal data sets become more prevalent, analytic approaches that account for multiple data categories and algorithms will be increasingly relevant. This study suggests that such approaches hold promise to guide researchers and clinicians in their quest to improve outcomes for devastating diseases like GBM.
590 $a School code: 0240.
650 4 $a Biology, Bioinformatics. $3 1018415
690 $a 0715
710 2 $a The University of Utah. $b Biomedical Informatics. $3 1681657
773 0 $t Dissertation Abstracts International $g 72-04B.
790 1 0 $a Frey, Lewis J., $e advisor
790 1 0 $a Bernard, Philip S. $e committee member
790 1 0 $a Cannon-Albright, Lisa $e committee member
790 1 0 $a Eilbeck, Karen $e committee member
790 1 0 $a Haug, Peter J. $e committee member
790 $a 0240
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3440218