Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data | Zendy

Zuyi Wang | Zendy; Yue Wang | Zendy; Jianhua Xuan | Zendy; Yibin Dong | Zendy; Marina Bakay | Zendy; Yuanjian Feng | Zendy; Robert Clarke | Zendy; Eric P. Hoffman | Zendy

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Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data

Author(s) -

Zuyi Wang,

Yue Wang,

Jianhua Xuan,

Yibin Dong,

Marina Bakay,

Yuanjian Feng,

Robert Clarke,

Eric P. Hoffman

Publication year - 2006

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btk036

Subject(s) - initialization , computer science , curse of dimensionality , perceptron , artificial intelligence , linear discriminant analysis , pattern recognition (psychology) , multilayer perceptron , bayes' theorem , artificial neural network , hyperparameter , naive bayes classifier , machine learning , support vector machine , data mining , bayesian probability , programming language

Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-fitting, and may not provide acceptable prediction accuracy.

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