Support vector machine classification and validation of cancer tissue samples using microarray expression data

By Terrence S. Furey, Nello Cristianini, Nigel P. Duffy, David W. Bednarski, Michèl Schummer, David Haussler. Bioinform., 2000

2,546 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5794141889d0e994c3103b0aaab08a18222c9c43

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Citations in the corpus

Human pluripotent stem cell-derived neural constructs for predicting neural toxicity (2015, background): “…demonstrated that accurate machine learning algorithms can be constructed with significantly fewer examples (data points or training compounds) than features (variables or genes) (44 – 46), but 60 training compounds is still an exceptionally small dataset given that ∼ 19,000 genes were assessed.”
Molecular classification of human carcinomas by use of gene expression signatures. (2001, neutral): “13; Fig.”
Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks (2001, methodology): “Thus far, these other methods have not been fully explored to extract the genes or features that are most important for the classification performance and which also will be of interest to cancer biologist”
A simple and efficient algorithm for gene selection using sparse logistic regression (2003, positive): “Furey et al. (2000) used Fisher score to perform feature selection prior to training.”
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights (2016, methodology): “The general objectives of meta-analysis include proposing new classifiers [10], comparing different classification methods [11], finding a common transcriptional profile [12], and evaluating generalization of prediction models across different studies”
Label-Noise Robust Logistic Regression and Its Applications (2012, background): “This contains expression levels of 2000 genes from 40 tumour and 22 normal colon tissues, and there is some evidence in the biological literature that label noise may be present [6, 15, 11, 21, 26].”