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Reverse-Engineering Gene Regulatory Networks from Microarray Data

Permanent Link: http://ncf.sobek.ufl.edu/NCFE003440/00001

Material Information

Title: Reverse-Engineering Gene Regulatory Networks from Microarray Data
Physical Description: Book
Language: English
Creator: Ryba, Tyrone R.
Publisher: New College of Florida
Place of Publication: Sarasota, Fla.
Creation Date: 2004
Publication Date: 2004

Subjects

Subjects / Keywords: Microarray
Gene Network
Reverse-Engineering
Genre: bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Though microarrays were developed nearly a decade ago, they have only begun to fulfill their potential. Advances in reproducibility and error control are slowly bringing a longtime goal � extracting causal regulatory information from gene expression data � within reach. Major computational hurdles currently prevent the application of mathematically precise methods to this task, but a wide-ranging collection of new and adapted systems from computational modeling are already achieving notable successes. The next task is to compare objectively the effectiveness of these methods in forming regulatory models from various types of data. This study aims to judge, partly by comparing false- negative and false-positive error rates in predicting known datasets, the relative efficacy of methods ranging from the well-established, technical differential equation models, to the relatively informal, ad-hoc event and edge detection methods. These algorithms are applied to synthetic gene expression profiles and yeast datasets in the Stanford Microarray Database (http://genorne-www.stanford.edu/microarray/) and Gene Expression Omnibus (http://www.ncbi.nlm.nih-gov/geo/) to define this effectiveness across various applications. In accord with previous studies, several methods retrieved synthetic gene connections quite well, but the percentage of known yeast transcriptional regulators recovered from real-world data remained at chance levels.
Statement of Responsibility: by Tyrone R. Ryba
Thesis: Thesis (B.A.) -- New College of Florida, 2004
Electronic Access: RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE
Bibliography: Includes bibliographical references.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Local: Faculty Sponsor: Clore, Amy

Record Information

Source Institution: New College of Florida
Holding Location: New College of Florida
Rights Management: Applicable rights reserved.
Classification: local - S.T. 2004 R9
System ID: NCFE003440:00001

Permanent Link: http://ncf.sobek.ufl.edu/NCFE003440/00001

Material Information

Title: Reverse-Engineering Gene Regulatory Networks from Microarray Data
Physical Description: Book
Language: English
Creator: Ryba, Tyrone R.
Publisher: New College of Florida
Place of Publication: Sarasota, Fla.
Creation Date: 2004
Publication Date: 2004

Subjects

Subjects / Keywords: Microarray
Gene Network
Reverse-Engineering
Genre: bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Though microarrays were developed nearly a decade ago, they have only begun to fulfill their potential. Advances in reproducibility and error control are slowly bringing a longtime goal � extracting causal regulatory information from gene expression data � within reach. Major computational hurdles currently prevent the application of mathematically precise methods to this task, but a wide-ranging collection of new and adapted systems from computational modeling are already achieving notable successes. The next task is to compare objectively the effectiveness of these methods in forming regulatory models from various types of data. This study aims to judge, partly by comparing false- negative and false-positive error rates in predicting known datasets, the relative efficacy of methods ranging from the well-established, technical differential equation models, to the relatively informal, ad-hoc event and edge detection methods. These algorithms are applied to synthetic gene expression profiles and yeast datasets in the Stanford Microarray Database (http://genorne-www.stanford.edu/microarray/) and Gene Expression Omnibus (http://www.ncbi.nlm.nih-gov/geo/) to define this effectiveness across various applications. In accord with previous studies, several methods retrieved synthetic gene connections quite well, but the percentage of known yeast transcriptional regulators recovered from real-world data remained at chance levels.
Statement of Responsibility: by Tyrone R. Ryba
Thesis: Thesis (B.A.) -- New College of Florida, 2004
Electronic Access: RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE
Bibliography: Includes bibliographical references.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Local: Faculty Sponsor: Clore, Amy

Record Information

Source Institution: New College of Florida
Holding Location: New College of Florida
Rights Management: Applicable rights reserved.
Classification: local - S.T. 2004 R9
System ID: NCFE003440:00001

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