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• W2912582129 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." @default.
• W2912582129 created "2019-02-21" @default.
• W2912582129 creator A5026513481 @default.
• W2912582129 creator A5049469999 @default.
• W2912582129 date "2019-06-01" @default.
• W2912582129 modified "2023-09-29" @default.
• W2912582129 title "A survey of neural network-based cancer prediction models from microarray data" @default.
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• W2912582129 doi "https://doi.org/10.1016/j.artmed.2019.01.006" @default.
• W2912582129 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30797633" @default.
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