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 ∼7 years. In 2006, researchers estimated that 0.

Prediction of protein secondary structure using probability based features and a hybrid system.

In this paper, we propose some co-occurrence probability-based features for prediction of protein secondary structure. The features are extracted using occurrence/nonoccurrence of secondary structures in the protein sequences. We explore two types of features: position-specific (based on position of amino acid on fragments of protein sequences) as well as position-independent (independent of amino acid position on fragments of protein sequences).

Prediction of protein folds: extraction of new features, dimensionality reduction, and fusion of heterogeneous classifiers.

Here, we consider a two-level (four classes in level 1 and 27 folds in level 2) protein fold determination problem. We propose several new features and use some existing features including frequencies of adjacent residues, frequencies of residues separated by one residue, and triplets (trio) of amino acid compositions (AACs). The dimensionality of the trio AAC features is drastically reduced using a neural network based novel online feature selection scheme.