Identification of a small set of plasma signalling proteins using neural network for prediction of Alzheimer's disease

Motivation: Alzheimer's disease (AD) is a dementia that gets worse with time resulting in loss of memory and cognitive functions. The life expectancy of AD patients following diagnosis is similar to 7 years. In 2006, researchers estimated that 0.40% of the world population (range 0.17-0.89%) was afflicted by AD, and that the prevalence rate would be tripled by 2050. Usually, examination of brain tissues is required for definite diagnosis of AD. So, it is crucial to diagnose AD at an early stage via some alternative methods. As the brain controls many functions via releasing signalling proteins through blood, we analyse blood plasma proteins for diagnosis of AD. Results: Here, we use a radial basis function (RBF) network for feature selection called feature selection RBF network for selection of plasma proteins that can help diagnosis of AD. We have identified a set of plasma proteins, smaller in size than previous study, with comparable prediction accuracy. We have also analysed mild cognitive impairment (MCI) samples with our selected proteins. We have used neural networks and support vector machines as classifiers. The principle component analysis, Sammmon projection and heat-map of the selected proteins have been used to demonstrate the proteins' discriminating power for diagnosis of AD. We have also found a set of plasma signalling proteins that can distinguish incipient AD from MCI at an early stage. Literature survey strongly supports the AD diagnosis capability of the selected plasma proteins.