@Article{brazma00,
  author  = {Brazma, Alvis and Vilo, Jaak},
  title   = {Gene expression data analysis},
  journal = {FEBS letters},
  year    = {2000},
  volume  = {480},
  number  = {1},
  pages   = {17--24},
}

@InProceedings{cheng00,
  author    = {Cheng, Yizong and Church, George M},
  title     = {Biclustering of expression data},
  booktitle = {Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology},
  year      = {2000},
  editor    = {Philip Bourne and Michael Gribskov and Russ Altman and Nancy Jensen and Debra Hope and Thomas Lengauer and Julie Mitchell and Eric Scheeff and Chris Smith and Shawn Strande and Helge Weissig},
  volume    = {8},
  pages     = {93--103},
  publisher = {AAAI Press, Menlo Park, CA},
}

@Article{lazzeroni02,
  author  = {Lazzeroni, Laura and Owen, Art and others},
  title   = {Plaid models for gene expression data},
  journal = {Statistica sinica},
  year    = {2002},
  volume  = {12},
  number  = {1},
  pages   = {61--86},
}

@Article{gu08,
  author  = {Gu, Jiajun and Liu, Jun S},
  title   = {Bayesian biclustering of gene expression data},
  journal = {BMC genomics},
  year    = {2008},
  volume  = {9},
  number  = {Suppl 1},
  pages   = {S4},
}

@InProceedings{kaiser09,
  author    = {Sebastian Kaiser and Friedrich Leisch},
  title     = {A Toolbox for Bicluster Analysis in {R}},
  booktitle = {{COMPSTAT} 2008: Proceedings in Computational Statistics},
  year      = {2008},
  editor    = {Paula Brito},
  volume    = {II},
  series    = {tech},
  pages     = {201--208},
  publisher = {Physica-Verlag, Heidelberg},
  abstract  = {Over the last decade, bicluster methods have become more and more popular in different fields of two way data analysis and a wide variety of algorithms and analysis methods have been published. In this paper we introduce the R package \verb1{1$\backslash$tt biclust\verb1}1, which contains a collection of bicluster algorithms, preprocessing methods for two way data, and validation and visualization techniques for bicluster results. For the first time, such a package is provided on a platform like R, where data analysts can easily add new bicluster algorithms and adapt them to their special needs.},
  keyword   = {Biclustering, Two-Way-Clustering, Software, {R}},
  url       = {http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-3293-7},
}

@Article{li09,
  author  = {Li, Guojun and Ma, Qin and Tang, Haibao and Paterson, Andrew H and Xu, Ying},
  title   = {{QUBIC}: a qualitative biclustering algorithm for analyses of gene expression data},
  journal = {Nucleic acids research},
  year    = {2009},
  volume  = {37},
  number  = {15},
  pages   = {e101},
}

@Article{smoot11,
  author  = {Smoot, Michael E and Ono, Keiichiro and Ruscheinski, Johannes and Wang, Peng-Liang and Ideker, Trey},
  title   = {Cytoscape 2.8: new features for data integration and network visualization},
  journal = {Bioinformatics},
  year    = {2011},
  volume  = {27},
  number  = {3},
  pages   = {431--432},
}

@Article{eren12,
  author   = {Eren, Kemal and Deveci, Mehmet and Küçüktunç, Onur and Çatalyürek, Ümit V.},
  title    = {A comparative analysis of biclustering algorithms for gene expression data},
  journal  = {Briefings in Bioinformatics},
  year     = {2012},
  abstract = {The need to analyze high-dimension biological data is driving the development of new data mining methods. Biclustering algorithms have been successfully applied to gene expression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of conditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms. Surveys and comparisons exist in the literature, but because of the large number and variety of biclustering algorithms, they are quickly outdated. In this article we partially address this problem of evaluating the strengths and weaknesses of existing biclustering methods. We used the BiBench package to compare 12 algorithms, many of which were recently published or have not been extensively studied. The algorithms were tested on a suite of synthetic data sets to measure their performance on data with varying conditions, such as different bicluster models, varying noise, varying numbers of biclusters and overlapping biclusters. The algorithms were also tested on eight large gene expression data sets obtained from the Gene Expression Omnibus. Gene Ontology enrichment analysis was performed on the resulting biclusters, and the best enrichment terms are reported. Our analyses show that the biclustering method and its parameters should be selected based on the desired model, whether that model allows overlapping biclusters, and its robustness to noise. In addition, we observe that the biclustering algorithms capable of finding more than one model are more successful at capturing biologically relevant clusters.},
  doi      = {10.1093/bib/bbs032},
  eprint   = {http://bib.oxfordjournals.org/content/early/2012/07/06/bib.bbs032.full.pdf+html},
  url      = {http://bib.oxfordjournals.org/content/early/2012/07/06/bib.bbs032.abstract},
}

@Article{zhou12,
  author    = {Zhou, Fengfeng and Ma, Qin and Li, Guojun and Xu, Ying},
  title     = {{QServer}: A Biclustering Server for Prediction and Assessment of Co-Expressed Gene Clusters},
  journal   = {PLoS ONE},
  year      = {2012},
  volume    = {7},
  number    = {3},
  pages     = {e32660},
  month     = {03},
  abstract  = {<sec> <title>Background</title> <p>Biclustering is a powerful technique for identification of co-expressed gene groups under any (unspecified) substantial subset of given experimental conditions, which can be used for elucidation of transcriptionally co-regulated genes.</p> </sec> <sec> <title>Results</title> <p>We have previously developed a biclustering algorithm, QUBIC, which can solve more general biclustering problems than previous biclustering algorithms. To fully utilize the analysis power the algorithm provides, we have developed a web server, QServer, for prediction, computational validation and analyses of co-expressed gene clusters. Specifically, the QServer has the following capabilities in addition to biclustering by QUBIC: (i) prediction and assessment of conserved <italic>cis</italic> regulatory motifs in promoter sequences of the predicted co-expressed genes; (ii) functional enrichment analyses of the predicted co-expressed gene clusters using Gene Ontology (GO) terms, and (iii) visualization capabilities in support of interactive biclustering analyses. QServer supports the biclustering and functional analysis for a wide range of organisms, including human, mouse, <italic>Arabidopsis</italic>, bacteria and archaea, whose underlying genome database will be continuously updated.</p> </sec> <sec> <title>Conclusion</title> <p>We believe that QServer provides an easy-to-use and highly effective platform useful for hypothesis formulation and testing related to transcription co-regulation.</p> </sec>},
  doi       = {10.1371/journal.pone.0032660},
  publisher = {Public Library of Science},
  url       = {http://dx.doi.org/10.1371%2Fjournal.pone.0032660},
}

@Article{szklarczyk14,
  author    = {Szklarczyk, Damian and Franceschini, Andrea and Wyder, Stefan and Forslund, Kristoffer and Heller, Davide and Huerta-Cepas, Jaime and Simonovic, Milan and Roth, Alexander and Santos, Alberto and Tsafou, Kalliopi P and others},
  title     = {{STRING} v10: protein--protein interaction networks, integrated over the tree of life},
  journal   = {Nucleic acids research},
  year      = {2014},
  volume    = {43},
  pages     = {D447--D452},
  doi       = {10.1093/nar/gku1003},
  publisher = {Oxford Univ Press},
}

@Article{fehrmann15,
  author  = {Fehrmann, Rudolf SN and Karjalainen, Juha M and Krajewska, Ma{\l}gorzata and Westra, Harm-Jan and Maloney, David and Simeonov, Anton and Pers, Tune H and Hirschhorn, Joel N and Jansen, Ritsert C and Schultes, Erik A and others},
  title   = {Gene expression analysis identifies global gene dosage sensitivity in cancer},
  journal = {Nature genetics},
  year    = {2015},
  volume  = {47},
  number  = {2},
  pages   = {115--125},
}

@Article{zhang16,
  author  = {Yu Zhang and Juan Xie and Jinyu Yang and Anne Fennell and Chi Zhang and Qin Ma},
  title   = {{QUBIC}: a bioconductor package for qualitative biclustering analysis of gene co- expression data},
  journal = {Bioinformatics},
  year    = {2017},
  volume  = {33},
  number  = {3},
  pages   = {450--452},
  doi     = {10.1093/bioinformatics/btw635},
}