NETLAB: algorithms for pattern recognition
NETLAB: algorithms for pattern recognition
Bayesian learning for neural networks
Bayesian learning for neural networks
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Soft data mining, computational theory of perceptions, and rough-fuzzy approach
Information Sciences: an International Journal - Special issue: Soft computing data mining
SNP discovery using advanced algorithms and neural networks
Bioinformatics
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Information Sciences: an International Journal
A review of Bayesian neural networks with an application to near infrared spectroscopy
IEEE Transactions on Neural Networks
Ant colony optimisation to identify genetic variant association with type 2 diabetes
Information Sciences: an International Journal
Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches
Information Sciences: an International Journal
| Hi-index | 0.07 |
Multiple sclerosis is an idiopathic inflammatory disease characterized by multiple focal lesions in the white matter of the central nervous system. Multiple sclerosis patients are usually treated with interferon-@b, but disease activity decrease in only 30-40% of patients. In the attempt to differentiate between responders and non-responders, we screened the main genes involved in the interferon signaling pathway for 38 single nucleotide polymorphisms (SNPs) in a multiple sclerosis Caucasian population from South Italy. We then analyzed the data using a multilayer perceptron neural network-based approach, in which we evaluated the global weight of a set of SNPs localized in different genes and their association with response to interferon therapy through a feature selection procedure (a combination of automatic relevance determination and backward elimination). The neural approach appears to be a useful tool in identifying gene polymorphisms involved in the response of patients to interferon therapy: 2 out of 5 genes were identified as containing 4 out of 38 significant single nucleotide polymorphisms, with a global accuracy of 70% in predicting responder and non-responder patients.