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Modeling cancer phenotypes with orde...

Afsari, Bahman.

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Modeling cancer phenotypes with order statistics of transcript data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling cancer phenotypes with order statistics of transcript data./
作者:	Afsari, Bahman.
面頁冊數:	182 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
Contained By:	Dissertation Abstracts International75-02B(E).
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3575002
ISBN:	9781303524011

Modeling cancer phenotypes with order statistics of transcript data.
Afsari, Bahman.

Modeling cancer phenotypes with order statistics of transcript data. - 182 p.

Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.

Thesis (Ph.D.)--The Johns Hopkins University, 2013.

This item is not available from ProQuest Dissertations & Theses.

A central component of "personalized medicine" and "translational bioinformatics" is the statistical analysis of high-throughput bio-molecular data. However, certain barriers have limited the application of the information extracted to clinical settings. For example, in the diagnosis and prognosis of cancer based on molecular measurements, two unresolved issues are the limited number of training samples and lack of any biological interpretation of the complex decision rules generated by standard methods in statistical learning.

ISBN: 9781303524011Subjects--Topical Terms:

1002712
Biostatistics.

Modeling cancer phenotypes with order statistics of transcript data.
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245 1 0 $a Modeling cancer phenotypes with order statistics of transcript data.
300 $a 182 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
500 $a Adviser: Donald Geman.
502 $a Thesis (Ph.D.)--The Johns Hopkins University, 2013.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a A central component of "personalized medicine" and "translational bioinformatics" is the statistical analysis of high-throughput bio-molecular data. However, certain barriers have limited the application of the information extracted to clinical settings. For example, in the diagnosis and prognosis of cancer based on molecular measurements, two unresolved issues are the limited number of training samples and lack of any biological interpretation of the complex decision rules generated by standard methods in statistical learning.
520 $a Motivated by this scenario, we focus in this thesis on the statistical analysis of molecular expression data gathered from cancer studies. We consider parsimonious rank-based methods, and construct models and predictors of disease phenotypes based on the ordering of the expression values of a small set of genes. In particular, we design rank discriminants with the property that the decision rule is determined by the set of participating genes. In addition, we incorporate prior biological knowledge to draw connections with molecular mechanisms by severely limiting the space of classifiers to those consistent with the structure of transcriptional and cell signaling networks, including interactions among transcription factors, RNA and proteins.
520 $a We also study two stochastic models for expression orderings. One is inspired by the Kendall tau distance over orderings, is related to the Mallow distribution on permutations, and provides a new family of classifiers based on likelihood ratio tests. The other model is the maximum entropy extension of the empirical pairwise comparison statistics, including an R package. Finally, we introduce a novel quantitative measure for pathway deregulation and new computational tools which generalize earlier work.
590 $a School code: 0098.
650 4 $a Biostatistics. $3 1002712
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650 4 $a Statistics. $3 517247
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710 2 $a The Johns Hopkins University. $b Electrical and Computer Engineering. $3 3174663
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793 $a English
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