Journal ArticleDOI
Revealing modular organization in the yeast transcriptional network
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TLDR
The approach assigns genes to context-dependent and potentially overlapping 'transcription modules', thus overcoming the main limitations of traditional clustering methods, and uses the method to elucidate regulatory properties of cellular pathways and to characterize cis-regulatory elements.Abstract:
Standard clustering methods can classify genes successfully when applied to relatively small data sets, but have limited use in the analysis of large-scale expression data, mainly owing to their assignment of a gene to a single cluster. Here we propose an alternative method for the global analysis of genome-wide expression data. Our approach assigns genes to context-dependent and potentially overlapping ‘transcription modules’, thus overcoming the main limitations of traditional clustering methods. We use our method to elucidate regulatory properties of cellular pathways and to characterize cis-regulatory elements. By applying our algorithm systematically to all of the available expression data on Saccharomyces cerevisiae, we identify a comprehensive set of overlapping transcriptional modules. Our results provide functional predictions for numerous genes, identify relations between modules and present a global view on the transcriptional network. articleread more
Citations
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Journal ArticleDOI
Network biology: understanding the cell's functional organization
TL;DR: This work states that rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize the view of biology and disease pathologies in the twenty-first century.
Journal ArticleDOI
Global analysis of protein localization in budding yeast
Won-Ki Huh,James V. Falvo,Luke C. Gerke,Adam S. Carroll,Russell W. Howson,Jonathan S. Weissman,Erin K. O'Shea +6 more
TL;DR: The construction and analysis of a collection of yeast strains expressing full-length, chromosomally tagged green fluorescent protein fusion proteins helps reveal the logic of transcriptional co-regulation, and provides a comprehensive view of interactions within and between organelles in eukaryotic cells.
Journal ArticleDOI
Global analysis of protein expression in yeast
Sina Ghaemmaghami,Won-Ki Huh,Kiowa Bower,Russell W. Howson,Archana Belle,Noah Dephoure,Erin K. O'Shea,Jonathan S. Weissman +7 more
TL;DR: A Saccharomyces cerevisiae fusion library is created where each open reading frame is tagged with a high-affinity epitope and expressed from its natural chromosomal location, and it is found that about 80% of the proteome is expressed during normal growth conditions.
BookDOI
Semi-Supervised Learning
TL;DR: Semi-supervised learning (SSL) as discussed by the authors is the middle ground between supervised learning (in which all training examples are labeled) and unsupervised training (where no label data are given).
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Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data
Eran Segal,Michael Y. Shapira,Aviv Regev,Aviv Regev,Dana Pe'er,David Botstein,Daphne Koller,Nir Friedman +7 more
TL;DR: The procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'.
References
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Journal ArticleDOI
Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Journal ArticleDOI
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Uri Alon,Naama Barkai,Daniel A. Notterman,Kurt C. Gish,S. Ybarra,David H. Mack,A. J. Levine,A. J. Levine +7 more
TL;DR: In this paper, a two-way clustering algorithm was applied to both the genes and the tissues, revealing broad coherent patterns that suggest a high degree of organization underlying gene expression in these tissues.
Journal ArticleDOI
Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation
Pablo Tamayo,Donna K. Slonim,Jill P. Mesirov,Qing Zhu,Sutisak Kitareewan,Ethan Dmitrovsky,Eric S. Lander,Todd R. Golub,Todd R. Golub +8 more
TL;DR: In this article, the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidi-mensional data, is described.
Proceedings Article
Biclustering of Expression Data
Yizong Cheng,George M. Church +1 more
TL;DR: An efficient node-deletion algorithm is introduced to find submatrices in expression data that have low mean squared residue scores and it is shown to perform well in finding co-regulation patterns in yeast and human.
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MIPS: a database for genomes and protein sequences
Hans-Werner Mewes,Dmitrij Frishman,Ulrich Güldener,Gertrud Mannhaupt,Klaus F. X. Mayer,Martin Mokrejs,Burkhard Morgenstern,Martin Münsterkötter,Stephen Rudd,B. Weil +9 more
TL;DR: This report describes the systematic and up-to-date analysis of genomes (PEDANT), a comprehensive database of the yeast genome (MYGD), a database reflecting the progress in sequencing the Arabidopsis thaliana genome (MATD), the database of assembled, annotated human EST clusters (MEST), and the collection of protein sequence data within the framework of the PIR-International Protein Sequence Database (described elsewhere in this volume).
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