Integrating network, sequence and functional features using machine learning approaches towards …

In the present study we have tried to identify potential Alz genes based on the extraction of their network, sequences and functional properties using machine learning approaches. We have carried out feature selection using seven different feature selection techniques along with PCA to extract significant features and used 11 machine learning classifiers to predict candidate Alz genes. To do so, we have obtained a list of known Alz-associated and NonAlz genes from the Entrez Gene database, which made the positive and negative dataset respectively. We also performed a series of docking studies followed by MD and MM/GBSA calculation and screened the already existing approved and investigational anti-Alzheimer drugs to identify drugs against novel candidate genes.Analysis of various biological features for Alz-associated and NonAlz genes Network features A total of nine…


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