@ARTICLE{Hinton1991-hintondiagram,
  title   = "Lesioning an Attractor Network: Investigations of Acquired
             Dyslexia",
  author  = "Hinton, Geoffrey E and Shallice, Tim",
  journal = "Psychological Review",
  volume  =  98,
  number  =  1,
  pages   = "74--95",
  year    =  1991
}


@ARTICLE{Bremner1994-hintonplots,
  title    = "Hinton diagrams: Viewing connection strengths in neural networks",
  author   = "Bremner, Frederick J and Gotts, Stephen J and Denham, Dina L",
  journal  = "Behav. Res. Methods Instrum. Comput.",
  volume   =  26,
  number   =  2,
  pages    = "215--218",
  month    =  jun,
  year     =  1994
}


@ARTICLE{Huber2015-orchestrating,
  title     = "Orchestrating high-throughput genomic analysis with Bioconductor",
  author    = "Huber, Wolfgang and Carey, Vincent J and Gentleman, Robert and
               Anders, Simon and Carlson, Marc and Carvalho, Benilton S and
               Bravo, Hector Corrada and Davis, Sean and Gatto, Laurent and
               Girke, Thomas and Gottardo, Raphael and Hahne, Florian and
               Hansen, Kasper D and Irizarry, Rafael a and Lawrence, Michael
               and Love, Michael I and Macdonald, James and Obenchain, Valerie
               and Ole{\'s}, Andrzej K and Pag{\`e}s, Herv{\'e} and Reyes,
               Alejandro and Shannon, Paul and Smyth, Gordon K and Tenenbaum,
               Dan and Waldron, Levi and Morgan, Martin",
  journal   = "Nat. Methods",
  publisher = "Nature Publishing Group",
  volume    =  12,
  number    =  2,
  pages     = "115--121",
  year      =  2015
}


@ARTICLE{Rue-Albrecht2018-isee,
  title    = "{iSEE}: Interactive {SummarizedExperiment} Explorer",
  author   = "Rue-Albrecht, Kevin and Marini, Federico and Soneson, Charlotte
              and Lun, Aaron T L",
  abstract = "Data exploration is critical to the comprehension of large
              biological data sets generated by high-throughput assays such as
              sequencing. However, most existing tools for interactive
              visualisation are limited to specific assays or analyses. Here,
              we present the iSEE (Interactive SummarizedExperiment Explorer)
              software package, which provides a general visual interface for
              exploring data in a SummarizedExperiment object. iSEE is directly
              compatible with many existing R/Bioconductor packages for
              analysing high-throughput biological data, and provides useful
              features such as simultaneous examination of (meta)data and
              analysis results, dynamic linking between plots and code tracking
              for reproducibility. We demonstrate the utility and flexibility
              of iSEE by applying it to explore a range of real transcriptomics
              and proteomics data sets.",
  journal  = "F1000Res.",
  volume   =  7,
  month    =  jun,
  year     =  2018
}