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      A survey of neural network-based cancer prediction models from microarray data

      Daoud, Maisa; Mayo, Michael
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      1-s2.0-S0933365717305067-main.pdf
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      DOI
       10.1016/j.artmed.2019.01.006
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      Daoud, M., & Mayo, M. (2019). A survey of neural network-based cancer prediction models from microarray data. Artificial Intelligence in Medicine, 97, 204–214. https://doi.org/10.1016/j.artmed.2019.01.006
      Permanent Research Commons link: https://hdl.handle.net/10289/12654
      Abstract
      Neural networks are powerful tools used widely for building cancer prediction models from microarray data. We review the most recently proposed models to highlight the roles of neural networks in predicting cancer from gene expression data. We identified articles published between 2013–2018 in scientific databases using keywords such as cancer classification, cancer analysis, cancer prediction, cancer clustering and microarray data. Analyzing the studies reveals that neural network methods have been either used for filtering (data engineering) the gene expressions in a prior step to prediction; predicting the existence of cancer, cancer type or the survivability risk; or for clustering unlabeled samples. This paper also discusses some practical issues that can be considered when building a neural network-based cancer prediction model. Results indicate that the functionality of the neural network determines its general architecture. However, the decision on the number of hidden layers, neurons, hypermeters and learning algorithm is made using trail-and-error techniques.
      Date
      2019
      Type
      Journal Article
      Publisher
      Elsevier
      Rights
      © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
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      • Computing and Mathematical Sciences Papers [1389]
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