A Methodology for the Detection of Relevant Single Nucleotide Polymorphism in Prostate Cancer by Means of Multivariate Adaptive Regression Splines and Backpropagation Artificial Neural Networks.
Juan Enrique Sánchez LasherasAdonina TardónGuillermo González TardónSergio Luis Suárez GómezVicente Martín SánchezCarmen González-DonquilesFrancisco Javier de Cos JuezPublished in: SOCO-CISIS-ICEUTE (2017)
Keyphrases
- back propagation
- artificial neural networks
- prostate cancer
- multivariate adaptive regression splines
- feed forward
- neural network
- feed forward neural networks
- multilayer perceptron
- hidden layer
- feedforward neural networks
- backpropagation neural networks
- mr images
- computer aided
- fuzzy logic
- high throughput
- activation function
- real time
- genome wide
- learning algorithm
- medical image analysis
- single nucleotide polymorphisms
- multi layer perceptron
- genetic algorithm
- neural network model
- training data
- image analysis
- human genome
- pattern recognition
- pairwise