The failure of proteins to fold correctly result in amyloidosis. Therefore, amyloid plaque prediction has become significant to narrow down the exploration of anti- amyloidosis and related drugs. In this research article, we propose a unique hybrid approach to computationally predict the formation of amyloid plaques by exploiting diversity in the feature vector extracted from protein sequences and structures. The diversity in the sequence of feature space is exploited using structure dependent features besides the physico-chemical information from amino acid chemistry and frequency spectrum based parameters. We explored the prediction capability with independent and integrated feature vectors by an ensemble machine learning classifier, Random Forests. Computational analysis evidence that the assimilation of diverse feature set outperform individual feature array with a balanced prediction accuracy of 0.830 and Receiver Characteristic Curve area of 0.918 on stratified10-fold cross-validation test.
All Science Journal Classification (ASJC) codes
- Molecular Biology