Feature Extraction In Gene Expression Dataset Using Multilayer Perceptron

Numerous amount of gene expression datasets that are publicly available have accumulated since decades. It is hence essential to recognize and extract the instances in terms quantitative qualitative means.In this study, Keras utilized model multilayer perceptron (MLP) features from given input dataset. The MLP extracts test after its initial training with top extracted classifiers. Finally features, fine tuned optimal namely Gene Expression database Normal Tumor tissues 2 (GENT2). experimental results shows proposed achieves better feature selection than other methods accuracy, f-measure, precision recall.