diss.bib

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@ARTICLE{Barabasi2012,
  author = {Barab\'asi, Albert-L\'aszl\'o},
  title = {The network takeover},
  journal = {Nat Phys},
  year = {2012},
  volume = {8},
  pages = {14--16},
  number = {1},
  month = {01},
  annote = {10.1038/nphys2188},
  bdsk-url-1 = {http://dx.doi.org/10.1038/nphys2188},
  date-added = {2012-07-28 13:39:05 +0200},
  date-modified = {2012-07-28 13:39:05 +0200},
  isbn = {1745-2473},
  m3 = {10.1038/nphys2188},
  owner = {bao},
  publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited.
	All Rights Reserved.},
  timestamp = {2012.07.28},
  ty = {JOUR},
  url = {http://dx.doi.org/10.1038/nphys2188}
}

@ARTICLE{Marbach2012,
  author = {Daniel Marbach and James C Costello and Robert K\"uffner and Nicole
	M Vega and Robert J Prill and Diogo M Camacho and Kyle R Allison
	and {The DREAM5 Consortium} and Manolis Kellis and James J Collins
	and Gustavo Stolovitzky},
  title = {Wisdom of crowds for robust gene network inference.},
  journal = {Nat Methods},
  year = {2012},
  volume = {9},
  pages = {796--804},
  number = {8},
  abstract = {Reconstructing gene regulatory networks from high-throughput data
	is a long-standing challenge. Through the Dialogue on Reverse Engineering
	Assessment and Methods (DREAM) project, we performed a comprehensive
	blind assessment of over 30 network inference methods on Escherichia
	coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico
	microarray data. We characterize the performance, data requirements
	and inherent biases of different inference approaches, and we provide
	guidelines for algorithm application and development. We observed
	that no single inference method performs optimally across all data
	sets. In contrast, integration of predictions from multiple inference
	methods shows robust and high performance across diverse data sets.
	We thereby constructed high-confidence networks for E. coli and S.
	aureus, each comprising ∼1,700 transcriptional interactions at a
	precision of ∼50\%. We experimentally tested 53 previously unobserved
	regulatory interactions in E. coli, of which 23 (43\%) were supported.
	Our results establish community-based methods as a powerful and robust
	tool for the inference of transcriptional gene regulatory networks.},
  doi = {10.1038/nmeth.2016},
  file = {Marbach2012.pdf:Marbach2012.pdf:PDF},
  institution = { Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
	[3].},
  language = {eng},
  medline-pst = {epublish},
  owner = {bao},
  pii = {nmeth.2016},
  pmid = {22796662},
  review = {sparsity constraints by regression methods tend to miss FFL and other
	motifs
	
	Bayesian methods usually below-average performance
	
	information theoretic methods better than correlation methods, but
	increase FPR in predicting cascades
	
	direct TF perturbation data greatly improve accuracy},
  timestamp = {2012.08.09},
  url = {http://dx.doi.org/10.1038/nmeth.2016}
}

@ARTICLE{Messina2004,
  author = {David N Messina and Jarret Glasscock and Warren Gish and Michael
	Lovett},
  title = {{An ORFeome-based analysis of human transcription factor genes and
	the construction of a microarray to interrogate their expression.}},
  journal = {Genome Res},
  year = {2004},
  volume = {14},
  pages = {2041--2047},
  number = {10B},
  month = {Oct},
  abstract = {Transcription factors (TFs) are essential regulators of gene expression,
	and mutated TF genes have been shown to cause numerous human genetic
	diseases. Yet to date, no single, comprehensive database of human
	TFs exists. In this work, we describe the collection of an essentially
	complete set of TF genes from one depiction of the human ORFeome,
	and the design of a microarray to interrogate their expression. Taking
	1468 known TFs from TRANSFAC, InterPro, and FlyBase, we used this
	seed set to search the ScriptSure human transcriptome database for
	additional genes. ScriptSure's genome-anchored transcript clusters
	allowed us to work with a nonredundant high-quality representation
	of the human transcriptome. We used a high-stringency similarity
	search by using BLASTN, and a protein motif search of the human ORFeome
	by using hidden Markov models of DNA-binding domains known to occur
	exclusively or primarily in TFs. Four hundred ninety-four additional
	TF genes were identified in the overlap between the two searches,
	bringing our estimate of the total number of human TFs to 1962. Zinc
	finger genes are by far the most abundant family (762 members), followed
	by homeobox (199 members) and basic helix-loop-helix genes (117 members).
	We designed a microarray of 50-mer oligonucleotide probes targeted
	to a unique region of the coding sequence of each gene. We have successfully
	used this microarray to interrogate TF gene expression in species
	as diverse as chickens and mice, as well as in humans.},
  doi = {10.1101/gr.2584104},
  institution = {Department of Genetics, Washington University School of Medicine,
	St. Louis, Missouri 63110, USA.},
  keywords = {Gene Expression Profiling; Genome, Human; Humans; Markov Chains; Oligonucleotide
	Array Sequence Analysis; Open Reading Frames, genetics; Transcription
	Factors, chemistry/genetics/metabolism},
  language = {eng},
  medline-pst = {ppublish},
  owner = {bao},
  pii = {14/10b/2041},
  pmid = {15489324},
  review = {The groups that sequenced the human
	
	genome estimated that there are between two and three thousand TFs
	in the human genome},
  timestamp = {2012.08.02},
  url = {http://dx.doi.org/10.1101/gr.2584104}
}

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