

  @Article{labelme,
  author = 	 {B. C. Russell and A. Torralba and K. P. Murphy and
                  W. T. Freeman},
  title = 	 {{LabelMe: a database and web-based tool for image
                  annotation}},
  journal = 	 {International Journal of Computer Vision},
  year = 	 2008,
  volume = 	 77,
  number = 	 {1--3},
  pages = 	 {157--173},
  month = 	 {May}}

@Book{weinberg,
  author = 	 {Robert A. Weinberg},
  title = 	 {{The Biology of Cancer}},
  publisher = 	 {Garland Science},
  year = 	 2006,
  edition = 	 {First},
  month = 	 {June}}

@Article{Bioconductor,
    author = {Robert C Gentleman and Vincent J. Carey and Douglas M. Bates and {others}},
    title = {Bioconductor: Open software development for computational biology and bioinformatics},
    journal = {Genome Biology},
    volume = {5},
    year = {2004},
    pages = {R80},
    url = {http://genomebiology.com/2004/5/10/R80},
  }


@article{R-Forge,
  author = {Stefan Theu\ss{}l and Achim Zeileis},
  title = {{Collaborative Software Development Using R-Forge}},
  journal = {The R Journal},
  year = 2009,
  volume = 1,
  number = 1,
  pages = {9--14},
  month = {May},
  url = {http://journal.r-project.org/2009-1/RJournal_2009-1_Theussl+Zeileis.pdf}
}


  @Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2011},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org/},
  }
 

@Article{penalized-cna,
  author = 	 {Zhongyang Zhang and Kenneth Lange and Roel Ophoff
                  and Chiara Sabatti},
  title = 	 {{Reconstructing DNA copy number by penalized
                  estimation and imputation}},
  journal = 	 {The Annals of Applied Statistics},
  year = 	 2010,
  volume = 	 4,
  pages = 	 {1749--1773}}

@Article{rtracklayer,
  author = 	 {Michael Lawrence and Robert Gentleman and Vincent Carey},
  title = 	 {{rtracklayer: an R package for interfacing with
                  genome browsers}},
  journal = 	 {Bioinformatics},
  year = 	 2009,
  volume = 	 25,
  number = 	 14,
  pages = 	 {1841--1842}}

@ARTICLE{cghweb,
AUTHOR = "Weil R. Lai and Vidhu Choudhary and Peter J. Park",
TITLE = "CGHweb: a tool for comparing DNA copy number segmentations from multiple algorithms.",
JOURNAL = "Bioinformatics",
PAGES = {1014-1015},
YEAR = {2008}  }

@article{cellprofiler,
author = {Jones, Thouis R. and Carpenter, Anne E. and Lamprecht,
                  Michael R. and Moffat, Jason and Silver, Serena
                  J. and Grenier, Jennifer K. and Castoreno, Adam
                  B. and Eggert, Ulrike S. and Root, David E. and
                  Golland, Polina and Sabatini, David M.}, 
title = {Scoring diverse cellular morphologies in image-based screens
                  with iterative feedback and machine learning}, 
volume = {106}, 
number = {6}, 
pages = {1826-1831}, 
year = {2009}, 
doi = {10.1073/pnas.0808843106}, 
abstract ={Many biological pathways were first uncovered by
                  identifying mutants with visible phenotypes and by
                  scoring every sample in a screen via tedious and
                  subjective visual inspection. Now, automated image
                  analysis can effectively score many phenotypes. In
                  practical application, customizing an image-analysis
                  algorithm or finding a sufficient number of example
                  cells to train a machine learning algorithm can be
                  infeasible, particularly when positive control
                  samples are not available and the phenotype of
                  interest is rare. Here we present a supervised
                  machine learning approach that uses iterative
                  feedback to readily score multiple subtle and
                  complex morphological phenotypes in high-throughput,
                  image-based screens. First, automated cytological
                  profiling extracts hundreds of numerical descriptors
                  for every cell in every image. Next, the researcher
                  generates a rule (i.e., classifier) to recognize
                  cells with a phenotype of interest during a short,
                  interactive training session using iterative
                  feedback. Finally, all of the cells in the
                  experiment are automatically classified and each
                  sample is scored based on the presence of cells
                  displaying the phenotype. By using this approach, we
                  successfully scored images in RNA interference
                  screens in 2 organisms for the prevalence of 15
                  diverse cellular morphologies, some of which were
                  previously intractable.}, 
URL = {http://www.pnas.org/content/106/6/1826.abstract}, 
eprint = {http://www.pnas.org/content/106/6/1826.full.pdf+html}, 
journal = {Proceedings of the National Academy of Sciences} 
}



@Article{haarseg,
  author = 	 {E. Ben-Yaacov and Y. C. Eldar},
  title = 	 {{A Fast and Flexible Method for the Segmentation of
                  aCGH Data}},
  journal = 	 {Bioinformatics},
  year = 	 2008,
  volume = 	 24,
  number = 	 16,
  pages = 	 {i139--i145},
  month = 	 {September}}

@article{isabelle-2009,
author = {Janoueix-Lerosey, Isabelle and Schleiermacher, Gudrun and Michels, Evi and Mosseri, Véronique and Ribeiro, Agnès and Lequin, Delphine and Vermeulen, Joëlle and Couturier, Jérôme and Peuchmaur, Michel and Valent, Alexander and Plantaz, Dominique and Rubie, Hervé and Valteau-Couanet, Dominique and Thomas, Caroline and Combaret, Valérie and Rousseau, Raphaël and Eggert, Angelika and Michon, Jean and Speleman, Frank and Delattre, Olivier}, 
title = {Overall Genomic Pattern Is a Predictor of Outcome in Neuroblastoma}, 
volume = {27}, 
number = {7}, 
pages = {1026-1033}, 
year = {2009}, 
doi = {10.1200/JCO.2008.16.0630}, 
abstract ={Purpose For a comprehensive overview of the genetic alterations of neuroblastoma, their association and clinical significance, we conducted a whole-genome DNA copy number analysis.Patients and Methods A series of 493 neuroblastoma (NB) samples was investigated by array-based comparative genomic hybridization in two consecutive steps (224, then 269 patients).Results Genomic analysis identified several types of profiles. Tumors presenting exclusively whole-chromosome copy number variations were associated with excellent survival. No disease-related death was observed in this group. In contrast, tumors with any type of segmental chromosome alterations characterized patients with a high risk of relapse. Patients with both numerical and segmental abnormalities clearly shared the higher risk of relapse of segmental-only patients. In a multivariate analysis, taking into account the genomic profile, but also previously described individual genetic and clinical markers with prognostic significance, the presence of segmental alterations with (HR, 7.3; 95% CI, 3.7 to 14.5; P < .001) or without MYCN amplification (HR, 4.5; 95% CI, 2.4 to 8.4; P < .001) was the strongest predictor of relapse; the other significant variables were age older than 18 months (HR, 1.8; 95% CI, 1.2 to 2.8; P = .004) and stage 4 (HR, 1.8; 95% CI, 1.2 to 2.7; P = .005). Finally, within tumors showing segmental alterations, stage 4, age, MYCN amplification, 1p and 11q deletions, and 1q gain were independent predictors of decreased overall survival.Conclusion The analysis of the overall genomic pattern, which probably unravels particular genomic instability mechanisms rather than the analysis of individual markers, is essential to predict relapse in NB patients. It adds critical prognostic information to conventional markers and should be included in future treatment stratification.}, 
URL = {http://jco.ascopubs.org/content/27/7/1026.abstract}, 
eprint = {http://jco.ascopubs.org/content/27/7/1026.full.pdf+html}, 
journal = {Journal of Clinical Oncology} 
}


@article{vamp,
author = {La Rosa, Philippe and Viara, Eric and Hup\'e, Philippe and Pierron, Gaëlle and Liva, Stéphane and Neuvial, Pierre and Brito, Isabel and Lair, Séverine and Servant, Nicolas and Robine, Nicolas and Manié, Elodie and Brennetot, Caroline and Janoueix-Lerosey, Isabelle and Raynal, Virginie and Gruel, Nadège and Rouveirol, Céline and Stransky, Nicolas and Stern, Marc-Henri and Delattre, Olivier and Aurias, Alain and Radvanyi, François and Barillot, Emmanuel}, 
title = {{VAMP: Visualization and analysis of array-CGH, 
transcriptome and other molecular profiles}}, 
volume = {22}, 
number = {17}, 
pages = {2066-2073}, 
year = {2006}, 
doi = {10.1093/bioinformatics/btl359}, 
abstract ={Motivation: Microarray-based CGH (Comparative Genomic Hybridization), transcriptome arrays and other large-scale genomic technologies are now routinely used to generate a vast amount of genomic profiles. Exploratory analysis of this data is crucial in helping to understand the data and to help form biological hypotheses. This step requires visualization of the data in a meaningful way to visualize the results and to perform first level analyses.Results: We have developed a graphical user interface for visualization and first level analysis of molecular profiles. It is currently in use at the Institut Curie for cancer research projects involving CGH arrays, transcriptome arrays, SNP (single nucleotide polymorphism) arrays, loss of heterozygosity results (LOH), and Chromatin ImmunoPrecipitation arrays (ChIP chips). The interface offers the possibility of studying these different types of information in a consistent way. Several views are proposed, such as the classical CGH karyotype view or genome-wide multi-tumor comparison. Many functionalities for analyzing CGH data are provided by the interface, including looking for recurrent regions of alterations, confrontation to transcriptome data or clinical information, and clustering. Our tool consists of PHP scripts and of an applet written in Java. It can be run on public datasets at http://bioinfo.curie.fr/vampAvailability: The VAMP software (Visualization and Analysis of array-CGH,transcriptome and other Molecular Profiles) is available upon request. It can be tested on public datasets at http://bioinfo.curie.fr/vamp. The documentation is available at http://bioinfo.curie.fr/vamp/docContact:vamp@curie.fr}, 
URL = {http://bioinformatics.oxfordjournals.org/content/22/17/2066.abstract}, 
eprint = {http://bioinformatics.oxfordjournals.org/content/22/17/2066.full.pdf+html}, 
journal = {Bioinformatics} 
}


@article{gudrun-jclinicaloncology,
author = {Schleiermacher, Gudrun and Janoueix-Lerosey, Isabelle and
                  Ribeiro, Agnes and Klijanienko, Jerzy and Couturier,
                  Jerome and Pierron, Gaelle and Mosseri, Veronique
                  and Valent, Alexander and Auger, Nathalie and
                  Plantaz, Dominique and Rubie, Herve and
                  Valteau-Couanet, Dominique and Bourdeaut, Franck and
                  Combaret, Valerie and Bergeron, Christophe and
                  Michon, Jean and Delattre, Olivier},
title = {Accumulation of Segmental Alterations Determines Progression in Neuroblastoma},
journal = {J Clin Oncol},
volume = {28},
number = {19},
pages = {3122-3130},
doi = {10.1200/JCO.2009.26.7955},
year = {2010},
abstract = {PurposeNeuroblastoma is characterized by two distinct
                  types of genetic profiles, consisting of either
                  numerical or segmental chromosome alterations. The
                  latter are associated with a higher risk of relapse,
                  even when occurring together with numerical
                  alterations. We explored the role of segmental
                  alterations in tumor progression and the possibility
                  of evolution from indolent to aggressive genomic
                  types.  Patients and MethodsArray-based comparative
                  genomic hybridization data of 394 neuroblastoma
                  samples were analyzed and linked to clinical data.
                  ResultsIntegration of ploidy and genomic data
                  indicated that pseudotriploid tumors with mixed
                  numerical and segmental profiles may be derived from
                  pseudotriploid tumors with numerical alterations
                  only. This was confirmed by the analysis of paired
                  samples, at diagnosis and at relapse, as in tumors
                  with a purely numerical profile at diagnosis
                  additional segmental alterations at relapse were
                  frequently observed. New segmental alterations at
                  relapse were also seen in patients with segmental
                  alterations at diagnosis. This was not linked to
                  secondary effects of cytotoxic treatments since it
                  occurred even in patients treated with surgery
                  alone. A higher number of chromosome breakpoints
                  were correlated with advanced age at diagnosis,
                  advanced stage of disease, with a higher risk of
                  relapse, and a poorer outcome.  ConclusionThese data
                  provide further evidence of the role of segmental
                  alterations, suggesting that tumor progression is
                  linked to the accumulation of segmental alterations
                  in neuroblastoma. This possibility of genomic
                  evolution should be taken into account in treatment
                  strategies of low- and intermediate-risk
                  neuroblastoma and should warrant biologic
                  reinvestigation at the time of relapse.  },
URL = {http://jco.ascopubs.org/cgi/content/abstract/28/19/3122},
eprint = {http://jco.ascopubs.org/cgi/reprint/28/19/3122.pdf}
}




@Inproceedings{jp-nips,
  author = 	 {Jean-Philippe Vert and Kevin Bleakley},
  title = 	 {{Fast detection of multiple change-points shared by
                  many signals using group LARS}},
  booktitle = 	 {Advances in Neural Information Processing Systems 23 (NIPS)},
  year = 	 2010,
  pages = {2343--2351},
editor = {J. Lafferty and C. K. I. Williams and J. Shawe-Taylor and R. S. Zemel and A. Cullota}
}

@article{guillem-joint,
author = {Picard, Franck and Lebarbier, Emilie and Hoebeke, Mark and Rigaill, Guillem and Thiam, Baba and Robin, Stéphane}, 
title = {{Joint segmentation, calling, and normalization of multiple CGH profiles}}, 
year = {2011}, 
doi = {10.1093/biostatistics/kxq076}, 
URL = {http://biostatistics.oxfordjournals.org/content/early/2011/01/05/biostatistics.kxq076.abstract}, 
eprint = {http://biostatistics.oxfordjournals.org/content/early/2011/01/05/biostatistics.kxq076.full.pdf+html}, 
journal = {Biostatistics},
pages = {413--428},
volume = 12,
number = 3
}

@Article{nbc,
AUTHOR = {Ritz, Anna and Paris, Pamela and Ittmann, Michael and Collins, Colin and Raphael, Benjamin},
TITLE = {Detection of recurrent rearrangement breakpoints from copy number data},
JOURNAL = {BMC Bioinformatics},
VOLUME = {12},
YEAR = {2011},
NUMBER = {1},
PAGES = {114},
URL = {http://www.biomedcentral.com/1471-2105/12/114},
DOI = {10.1186/1471-2105-12-114},
PubMedID = {21510904},
ISSN = {1471-2105},
ABSTRACT = {BACKGROUND:Copy number variants (CNVs), including
                  deletions, amplifications, and other rearrangements,
                  are common in human and cancer genomes. Copy number
                  data from array comparative genome hybridization
                  (aCGH) and next-generation DNA sequencing is widely
                  used to measure copy number variants. Comparison of
                  copy number data from multiple individuals reveals
                  recurrent variants. Typically, the interior of a
                  recurrent CNV is examined for genes or other loci
                  associated with a phenotype. However, in some cases,
                  such as gene truncations and fusion genes, the
                  target of variant lies at the boundary of the
                  variant.RESULTS:We introduce Neighborhood Breakpoint
                  Conservation (NBC), an algorithm for identifying
                  rearrangement breakpoints that are highly conserved
                  at the same locus in multiple individuals. NBC
                  detects recurrent breakpoints at varying levels of
                  resolution, including breakpoints whose location is
                  exactly conserved and breakpoints whose location
                  varies within a gene. NBC also identifies pairs of
                  recurrent breakpoints such as those that result from
                  fusion genes. We apply NBC to aCGH data from 36
                  primary prostate tumors and identify 12 novel
                  rearrangements, one of which is the well-known
                  TMPRSS2-ERG fusion gene. We also apply NBC to 227
                  glioblastoma tumors and predict 93 novel
                  rearrangements which we further classify as gene
                  truncations, germline structural variants, and
                  fusion genes. A number of these variants involve the
                  protein phosphatase PTPN12 suggesting that
                  deregulation of PTPN12, via a variety of
                  rearrangements, is common in
                  glioblastoma.CONCLUSIONS:We demonstrate that NBC is
                  useful for detection of recurrent breakpoints
                  resulting from copy number variants or other
                  structural variants, and in particular identifies
                  recurrent breakpoints that result in gene
                  truncations or fusion genes. Software is available
                  at
                  http://http.//cs.brown.edu/people/braphael/software.html.},
}


@Article{compare,
  author = 	 {Hanni Willenbrock and Jane Fridlyand},
  title = 	 {{A comparison study: applying segmentation to array
                  CGH data for downstream analysis}},
  journal = 	 {Bioinformatics},
  year = 	 2005,
  volume = 	 21,
  number = 	 22,
  pages = 	 {4084--4091}}



@Article{lavielle2005,
  author = 	 {Marc Lavielle},
  title = 	 {Using penalized contrasts for the change-point problem},
  journal = 	 {Signal Processing},
  year = 	 2005,
  volume = 	 85,
  pages = 	 {1501--1510}}

@Article{cghseg,
  author = 	 {Franck Picard and Stephane Robin and Marc Lavielle
                  and Christian Vaisse and Jean-Jacques Daudin},
  title = 	 {{A statistical approach for array CGH data analysis}},
  journal = 	 {BMC Bioinformatics},
  year = 	 2005,
  volume = 	 6,
  number = 	 27}

@Article{glad,
  author = 	 {Philippe Hup\'e and Nicolas Stransky and Jean-Paul
                  Thiery and François Radvanyi and Emmanuel
                  Barillot},
  title = 	 {{Analysis of array CGH data: from signal ratio to
                  gain and loss of DNA regions}},
  journal = 	 {Bioinformatics},
  year = 	 2004,
  volume = 	 20,
  number = 	 18,
  pages = 	 {3413--3422}
}

@Article{shah,
  author = 	 {Sohrab P. Shah and Xiang Xuan and Ron J. DeLeeuw and
                  Mehrnoush Khojasteh and Wan L. Lam and Raymond Ng
                  and Kevin P. Murphy},
  title = 	 {{Integrating copy number polymorphisms into array CGH
                  analysis using a robust HMM}},
  journal = 	 {Bioinformatics},
  year = 	 2006,
  volume = 	 22,
  number = 	 14,
  pages = 	 {431--439}}

@MISC{glmnet,
    author = {Jerome Friedman and Trevor Hastie and Rob Tibshirani},
    title = {Regularization paths for generalized linear models via coordinate descent },
    year = {2009}
}

@Article{mBIC,
  author = 	 {Nancy R. Zhang and David O. Siegmund},
  title = 	 {{A Modified Bayes Information Criterion with
                  Applications to the Analysis of Comparative Genomic
                  Hybridization Data}},
  journal = 	 {Biometrics},
  year = 	 2007,
  volume = 	 63,
  pages = 	 {22--32}}

@article{dnacopy,
    address = {Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021, USA.},
    author = {Venkatraman, E. S. and Olshen, Adam B.},
    doi = {10.1093/bioinformatics/btl646},
    issn = {1367-4811},
    journal = {Bioinformatics},
    keywords = {cgh},
    month = mar,
    number = {6},
    pages = {657--663},
    pmid = {17234643},
    title = {{A faster circular binary segmentation algorithm for the analysis of array CGH data}},
    url = {http://dx.doi.org/10.1093/bioinformatics/btl646},
    volume = {23},
    year = {2007}
}
@INPROCEEDINGS{milf,
    author = {Oded Maron and Tomás Lozano-Pérez},
    title = {A Framework for Multiple-Instance Learning},
    booktitle = {ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS},
    year = {1998},
    pages = {570--576},
    publisher = {MIT Press}
}

@INPROCEEDINGS{svm-milf,
    author = {Stuart Andrews and Ioannis Tsochantaridis and Thomas Hofmann},
    title = {Support vector machines for multiple-instance learning},
    booktitle = {Advances in Neural Information Processing Systems 15},
    year = {2003},
    pages = {561--568},
    publisher = {MIT Press}
}
@Article{cghFLasso,
  author = 	 {Robert Tibshirani and Pei Wang},
  title = 	 {Spatial smoothing and hot spot detection for {CGH}
                  data using the fused lasso},
  journal = 	 {Biostatistics},
  year = 	 2007}

@unpublished{fused-lasso-path,
author = {H. Hoefling},
title = {{A path algorithm for the Fused Lasso Signal Approximator}},
year = 2009,
eprint = {arXiv:0910.0526},
note = {arXiv:0910.0526}
}
@unpublished{pruned-dp,
author = {Guillem Rigaill},
title = {Pruned dynamic programming for optimal multiple change-point
                  detection},
year = 2010,
eprint = {arXiv:1004.0887},
note = {arXiv:1004.0887}
}
