Integrating naive Bayes models and external knowledge to examine copper and iron homeostasis in S. cerevisiae.

E. J. Moler, Derek C Radisky, I. S. Mian

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

A novel suite of analytical techniques and visualization tools are applied to 78 published transcription profiling experiments monitoring 5,687 Saccharomyces cerevisiae genes in studies examining cell cycle, responses to stress, and diauxic shift. A naive Bayes model discovered and characterized 45 classes of gene profile vectors. An enrichment measure quantified the association between these classes and specific external knowledge defined by four sets of categories to which genes can be assigned: 106 protein functions, 5 stages of the cell cycle, 265 transcription factors, and 16 chromosomal locations. Many of the 38 genes in class 42 are known to play roles in copper and iron homeostasis. The 17 uncharacterized open reading frames in this class may be involved in similar homeostatic processes; human homologs of two of them could be associated with as yet undefined disease states arising from aberrant metal ion regulation. The Met4, Met31, and Met32 transcription factors may play a role in coregulating genes involved in copper and iron metabolism. Extensions of the simple graphical model used for clustering to learning more complex models of genetic networks are discussed.

Original languageEnglish (US)
Pages (from-to)127-135
Number of pages9
JournalPhysiol Genomics
Volume4
Issue number2
StatePublished - Dec 18 2000
Externally publishedYes

Fingerprint

Saccharomyces cerevisiae
Copper
Homeostasis
Iron
Genes
Cell Cycle
Transcription Factors
Genetic Models
Open Reading Frames
Cluster Analysis
Metals
Learning
Ions
Proteins

ASJC Scopus subject areas

  • Physiology
  • Genetics

Cite this

Integrating naive Bayes models and external knowledge to examine copper and iron homeostasis in S. cerevisiae. / Moler, E. J.; Radisky, Derek C; Mian, I. S.

In: Physiol Genomics, Vol. 4, No. 2, 18.12.2000, p. 127-135.

Research output: Contribution to journalArticle

@article{22846a8ca37c42d1b9eb41647aebb619,
title = "Integrating naive Bayes models and external knowledge to examine copper and iron homeostasis in S. cerevisiae.",
abstract = "A novel suite of analytical techniques and visualization tools are applied to 78 published transcription profiling experiments monitoring 5,687 Saccharomyces cerevisiae genes in studies examining cell cycle, responses to stress, and diauxic shift. A naive Bayes model discovered and characterized 45 classes of gene profile vectors. An enrichment measure quantified the association between these classes and specific external knowledge defined by four sets of categories to which genes can be assigned: 106 protein functions, 5 stages of the cell cycle, 265 transcription factors, and 16 chromosomal locations. Many of the 38 genes in class 42 are known to play roles in copper and iron homeostasis. The 17 uncharacterized open reading frames in this class may be involved in similar homeostatic processes; human homologs of two of them could be associated with as yet undefined disease states arising from aberrant metal ion regulation. The Met4, Met31, and Met32 transcription factors may play a role in coregulating genes involved in copper and iron metabolism. Extensions of the simple graphical model used for clustering to learning more complex models of genetic networks are discussed.",
author = "Moler, {E. J.} and Radisky, {Derek C} and Mian, {I. S.}",
year = "2000",
month = "12",
day = "18",
language = "English (US)",
volume = "4",
pages = "127--135",
journal = "Physiological Genomics",
issn = "1094-8341",
publisher = "American Physiological Society",
number = "2",

}

TY - JOUR

T1 - Integrating naive Bayes models and external knowledge to examine copper and iron homeostasis in S. cerevisiae.

AU - Moler, E. J.

AU - Radisky, Derek C

AU - Mian, I. S.

PY - 2000/12/18

Y1 - 2000/12/18

N2 - A novel suite of analytical techniques and visualization tools are applied to 78 published transcription profiling experiments monitoring 5,687 Saccharomyces cerevisiae genes in studies examining cell cycle, responses to stress, and diauxic shift. A naive Bayes model discovered and characterized 45 classes of gene profile vectors. An enrichment measure quantified the association between these classes and specific external knowledge defined by four sets of categories to which genes can be assigned: 106 protein functions, 5 stages of the cell cycle, 265 transcription factors, and 16 chromosomal locations. Many of the 38 genes in class 42 are known to play roles in copper and iron homeostasis. The 17 uncharacterized open reading frames in this class may be involved in similar homeostatic processes; human homologs of two of them could be associated with as yet undefined disease states arising from aberrant metal ion regulation. The Met4, Met31, and Met32 transcription factors may play a role in coregulating genes involved in copper and iron metabolism. Extensions of the simple graphical model used for clustering to learning more complex models of genetic networks are discussed.

AB - A novel suite of analytical techniques and visualization tools are applied to 78 published transcription profiling experiments monitoring 5,687 Saccharomyces cerevisiae genes in studies examining cell cycle, responses to stress, and diauxic shift. A naive Bayes model discovered and characterized 45 classes of gene profile vectors. An enrichment measure quantified the association between these classes and specific external knowledge defined by four sets of categories to which genes can be assigned: 106 protein functions, 5 stages of the cell cycle, 265 transcription factors, and 16 chromosomal locations. Many of the 38 genes in class 42 are known to play roles in copper and iron homeostasis. The 17 uncharacterized open reading frames in this class may be involved in similar homeostatic processes; human homologs of two of them could be associated with as yet undefined disease states arising from aberrant metal ion regulation. The Met4, Met31, and Met32 transcription factors may play a role in coregulating genes involved in copper and iron metabolism. Extensions of the simple graphical model used for clustering to learning more complex models of genetic networks are discussed.

UR - http://www.scopus.com/inward/record.url?scp=0034684814&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034684814&partnerID=8YFLogxK

M3 - Article

VL - 4

SP - 127

EP - 135

JO - Physiological Genomics

JF - Physiological Genomics

SN - 1094-8341

IS - 2

ER -