@phdthesis{meyer2008information,
Author = {Patrick Emmanual Meyer},
Date-Added = {2009-01-29 19:45:54 +0100},
Date-Modified = {2009-01-29 19:48:51 +0100},
Month = {December},
School = {Universit\'{e} Libre de Bruxelles},
Title = {Information-Theoretic Variable Selection and Network Inference from Microarray Data},
Year = {2008},
Bdsk-Url-1 = {http://theses.ulb.ac.be/ETD-db/collection/available/ULBetd-12162008-093634/}}

@article{reuter2000targeting,
Abstract = {A series of alterations in the cellular genome affecting the expression or function of genes controlling cell growth and differentiation is considered to be the main cause of cancer. These mutational events include activation of oncogenes and inactivation of tumor suppressor genes. The elucidation of human cancer at the molecular level allows the design of rational, mechanism-based therapeutic agents that antagonize the specific activity of biochemical processes that are essential to the malignant phenotype of cancer cells. Because the frequency of RAS mutations is among the highest for any gene in human cancers, development of inhibitors of the Ras-mitogen-activated protein kinase pathway as potential anticancer agents is a very promising pharmacologic strategy. Inhibitors of Ras signaling have been shown to revert Ras-dependent transformation and cause regression of Ras-dependent tumors in animal models. The most promising new class of these potential cancer therapeutics are the farnesyltransferase inhibitors. The development of these compounds has been driven by the observation that oncogenic Ras function is dependent upon posttranslational modification, which enables membrane binding. In contrast to many conventional chemotherapeutics, farnesyltransferase inhibitors are remarkably specific and have been demonstrated to cause no gross systemic toxicity in animals. Some orally bioavailable inhibitors are presently being evaluated in phase II clinical trials. This review presents an overview on some inhibitors of the Ras signaling pathway, including their specificity and effectiveness in vivo. Because Ras signaling plays a crucial role in the pathogenesis of some hematologic malignancies, the potential therapeutic usefulness of these inhibitors is discussed. (Blood. 2000;96:1655-1669)},
Author = {Reuter, C W and Morgan, M A and Bergmann, L},
Date-Added = {2011-05-22 15:01:26 -0400},
Date-Modified = {2011-05-22 15:01:37 -0400},
Journal = {Blood},
Journal-Full = {Blood},
Mesh = {Alkyl and Aryl Transferases; Animals; Enzyme Inhibitors; Farnesyltranstransferase; Hematologic Neoplasms; Humans; MAP Kinase Signaling System; Signal Transduction; ras Proteins},
Month = {Sep},
Number = {5},
Pages = {1655-69},
Pmid = {10961860},
Pst = {ppublish},
Title = {Targeting the Ras signaling pathway: a rational, mechanism-based treatment for hematologic malignancies?},
Volume = {96},
Year = {2000}}

@article{jorissen2009metastasisassociated,
Abstract = {PURPOSE: Colorectal cancer prognosis is currently predicted from pathologic staging, providing limited discrimination for Dukes stage B and C disease. Additional markers for outcome are required to help guide therapy selection for individual patients. EXPERIMENTAL DESIGN: A multisite single-platform microarray study was done on 553 colorectal cancers. Gene expression changes were identified between stage A and D tumors (three training sets) and assessed as a prognosis signature in stage B and C tumors (independent test and external validation sets). RESULTS: One hundred twenty-eight genes showed reproducible expression changes between three sets of stage A and D cancers. Using consistent genes, stage B and C cancers clustered into two groups resembling early-stage and metastatic tumors. A Prediction Analysis of Microarray algorithm was developed to classify individual intermediate-stage cancers into stage A-like/good prognosis or stage D-like/poor prognosis types. For stage B patients, the treatment adjusted hazard ratio for 6-year recurrence in individuals with stage D-like cancers was 10.3 (95\% confidence interval, 1.3-80.0; P = 0.011). For stage C patients, the adjusted hazard ratio was 2.9 (95\% confidence interval, 1.1-7.6; P = 0.016). Similar results were obtained for an external set of stage B and C patients. The prognosis signature was enriched for downregulated immune response genes and upregulated cell signaling and extracellular matrix genes. Accordingly, sparse tumor infiltration with mononuclear chronic inflammatory cells was associated with poor outcome in independent patients. CONCLUSIONS: Metastasis-associated gene expression changes can be used to refine traditional outcome prediction, providing a rational approach for tailoring treatments to subsets of patients. (Clin Cancer Res 2009;15(24):7642-51).},
Author = {Jorissen, Robert N and Gibbs, Peter and Christie, Michael and Prakash, Saurabh and Lipton, Lara and Desai, Jayesh and Kerr, David and Aaltonen, Lauri A and Arango, Diego and Kruh{\o}ffer, Mogens and Orntoft, Torben F and Andersen, Claus Lindbjerg and Gruidl, Mike and Kamath, Vidya P and Eschrich, Steven and Yeatman, Timothy J and Sieber, Oliver M},
Date-Added = {2011-05-22 15:15:04 -0400},
Date-Modified = {2011-05-22 15:15:16 -0400},
Doi = {10.1158/1078-0432.CCR-09-1431},
Journal = {Clin Cancer Res},
Journal-Full = {Clinical cancer research : an official journal of the American Association for Cancer Research},
Month = {Dec},
Number = {24},
Pages = {7642-7651},
Pmc = {PMC2920750},
Pmid = {19996206},
Pst = {ppublish},
Title = {Metastasis-Associated Gene Expression Changes Predict Poor Outcomes in Patients with Dukes Stage B and C Colorectal Cancer},
Volume = {15},
Year = {2009},
Bdsk-Url-1 = {http://dx.doi.org/10.1158/1078-0432.CCR-09-1431}}

@article{mccall2010frozen,
Abstract = {Robust multiarray analysis (RMA) is the most widely used preprocessing algorithm for Affymetrix and Nimblegen gene expression microarrays. RMA performs background correction, normalization, and summarization in a modular way. The last 2 steps require multiple arrays to be analyzed simultaneously. The ability to borrow information across samples provides RMA various advantages. For example, the summarization step fits a parametric model that accounts for probe effects, assumed to be fixed across arrays, and improves outlier detection. Residuals, obtained from the fitted model, permit the creation of useful quality metrics. However, the dependence on multiple arrays has 2 drawbacks: (1) RMA cannot be used in clinical settings where samples must be processed individually or in small batches and (2) data sets preprocessed separately are not comparable. We propose a preprocessing algorithm, frozen RMA (fRMA), which allows one to analyze microarrays individually or in small batches and then combine the data for analysis. This is accomplished by utilizing information from the large publicly available microarray databases. In particular, estimates of probe-specific effects and variances are precomputed and frozen. Then, with new data sets, these are used in concert with information from the new arrays to normalize and summarize the data. We find that fRMA is comparable to RMA when the data are analyzed as a single batch and outperforms RMA when analyzing multiple batches. The methods described here are implemented in the R package fRMA and are currently available for download from the software section of http://rafalab.jhsph.edu.},
Author = {McCall, Matthew N and Bolstad, Benjamin M and Irizarry, Rafael A},
Date-Added = {2011-05-18 10:03:23 -0400},
Date-Modified = {2011-05-18 10:03:56 -0400},
Doi = {10.1093/biostatistics/kxp059},
Journal = {Biostatistics},
Journal-Full = {Biostatistics (Oxford, England)},
Mesh = {Algorithms; Biometry; Databases, Genetic; Humans; Internet; Oligonucleotide Array Sequence Analysis; Software},
Month = {Apr},
Number = {2},
Pages = {242-53},
Pmc = {PMC2830579},
Pmid = {20097884},
Pst = {ppublish},
Title = {Frozen robust multiarray analysis (fRMA)},
Volume = {11},
Year = {2010},
Bdsk-Url-1 = {http://dx.doi.org/10.1093/biostatistics/kxp059}}


@article{ding2005minimum,
Abstract = {How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. We propose a minimum redundancy - maximum relevance (MRMR) feature selection framework. Genes selected via MRMR provide a more balanced coverage of the space and capture broader characteristics of phenotypes. They lead to significantly improved class predictions in extensive experiments on 6 gene expression data sets: NCI, Lymphoma, Lung, Child Leukemia, Leukemia, and Colon. Improvements are observed consistently among 4 classification methods: Naive Bayes, Linear discriminant analysis, Logistic regression, and Support vector machines. SUPPLIMENTARY: The top 60 MRMR genes for each of the datasets are listed in http://crd.lbl.gov/~cding/MRMR/. More information related to MRMR methods can be found at http://www.hpeng.net/.},
Author = {Ding, Chris and Peng, Hanchuan},
Date-Added = {2011-02-22 09:21:06 -0500},
Date-Modified = {2011-02-22 09:21:23 -0500},
Journal = {J Bioinform Comput Biol},
Journal-Full = {Journal of bioinformatics and computational biology},
Mesh = {Algorithms; Discriminant Analysis; Gene Expression Profiling; Oligonucleotide Array Sequence Analysis; Pattern Recognition, Automated},
Month = {Apr},
Number = {2},
Pages = {185-205},
Pmid = {15852500},
Pst = {ppublish},
Title = {Minimum redundancy feature selection from microarray gene expression data},
Volume = {3},
Year = {2005}}

@article{meyer2007information,
Abstract = {The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR), an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes) network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.},
Author = {Meyer, Patrick E and Kontos, Kevin and Lafitte, Frederic and Bontempi, Gianluca},
Date-Added = {2009-08-03 14:49:12 +0200},
Date-Modified = {2009-08-03 14:49:24 +0200},
Doi = {10.1155/2007/79879},
Journal = {EURASIP J Bioinform Syst Biol},
Journal-Full = {EURASIP journal on bioinformatics \& systems biology},
Pages = {79879},
Pmid = {18354736},
Title = {Information-theoretic inference of large transcriptional regulatory networks},
Year = {2007},
Bdsk-Url-1 = {http://dx.doi.org/10.1155/2007/79879}}

@article{cheng2002learning,
Author = {Jie Cheng and Russell Greiner and Jonathan Kelly and David Bell and Weiru Liu},
Date-Added = {2011-02-22 09:32:30 -0500},
Date-Modified = {2011-05-23 09:08:17 -0400},
Issn = {0004-3702},
Journal = {Artificial Intelligence},
Number = {1-2},
Pages = {43 - 90},
Title = {Learning Bayesian networks from data: An information-theory based approach},
Url = {http://www.sciencedirect.com/science/article/B6TYF-45BCRWV-2/2/390d83eb89efc5e0bc8de9e43e0681d9},
Volume = {137},
Year = {2002},
Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/B6TYF-45BCRWV-2/2/390d83eb89efc5e0bc8de9e43e0681d9},
Bdsk-Url-2 = {http://dx.doi.org/10.1016/S0004-3702(02)00191-1}}

@inproceedings{olsen2009inferring,
Author = {C. Olsen and P. E. Meyer and G. Bontempi},
Booktitle = {Proceedings of the 5th Benelux Bioinformatics Conference (BBC09)},
Date-Added = {2011-02-22 09:24:52 -0500},
Date-Modified = {2011-02-22 09:26:01 -0500},
Title = {Inferring causal relationships using information-theoretic measures},
Year = {2009}}



@book{neopalitan2003learning,
Date-Added = {2011-02-22 11:26:36 -0500},
Date-Modified = {2011-02-22 11:27:08 -0500},
Publisher = {Prentica Hall},
Title = {Learning Bayesian Networks},
Year = {2003}}

@article{mcgill1954multivariate,
author = {McGill, W J},
title = {{Multivariate information transmission}},
journal = {Psychometrika},
year = {1954}
}


@article{smoot2011cytoscape,
Abstract = {Cytoscape is a popular bioinformatics package for biological network visualization and data integration. Version 2.8 introduces two powerful new features--Custom Node Graphics and Attribute Equations--which can be used jointly to greatly enhance Cytoscape's data integration and visualization capabilities. Custom Node Graphics allow an image to be projected onto a node, including images generated dynamically or at remote locations. Attribute Equations provide Cytoscape with spreadsheet-like functionality in which the value of an attribute is computed dynamically as a function of other attributes and network properties. Availability and implementation: Cytoscape is a desktop Java application released under the Library Gnu Public License (LGPL). Binary install bundles and source code for Cytoscape 2.8 are available for download from http://cytoscape.org.},
Author = {Smoot, Michael E and Ono, Keiichiro and Ruscheinski, Johannes and Wang, Peng-Liang and Ideker, Trey},
Date-Added = {2011-05-22 20:43:21 -0400},
Date-Modified = {2011-05-22 20:43:33 -0400},
Doi = {10.1093/bioinformatics/btq675},
Journal = {Bioinformatics},
Journal-Full = {Bioinformatics (Oxford, England)},
Month = {Feb},
Number = {3},
Pages = {431-2},
Pmc = {PMC3031041},
Pmid = {21149340},
Pst = {ppublish},
Title = {Cytoscape 2.8: new features for data integration and network visualization},
Volume = {27},
Year = {2011},
Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/btq675}}


@article{bild2006oncogenic,
Author = {A. H. Bild and G. Yao and J. T. Chang and Q. Wang and A. Potti and D. Chasse and M-B. Joshi and >D. Harpole and J. M. Lancaster and A. Berschuk and J. A. {Olson Jr} and J. R. Marks and H. K. Dressman and M. West and J. R. Nevins},
Date-Added = {2007-11-28 10:22:32 +0100},
Date-Modified = {2007-11-28 10:27:02 +0100},
Journal = {Nature},
Pages = {353-356},
Title = {Oncogenic pathway signatures in human cancers as a guide to targeted therapies},
Volume = {439},
Year = {2006}}

@article{niranjan2008study,
Author = {Niranjan, V and Mahmood, R and kalaivan, A and Sudha S},
Date-Added = {2011-05-22 21:04:17 -0400},
Date-Modified = {2011-05-22 21:06:05 -0400},
Journal = {Journal of Theoretical and Applied Information Technology   },
Number = {1},
Pages = {31--55},
Title = {Study of cancer gene in X-chromosome},
Volume = {4},
Year = {2008}}