Feature Extraction In Gene Expression Dataset Using Multilayer Perceptron

Abstract

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

Toward Improved Classification of Perceived Stress Using Time Domain Features

“…Neeraj et al extracted time domain features from the combined biological signal to classify them [21]. Techniques for extracting statistical features in the time domain, including mean correlation, kurtosis, and skewness, are employed for classification using the KNN classifier [22]. The outcomes are juxtaposed with features obtained through conventional Spatial Patterns, which are then classified utilizing the Linear Discriminant Analysis Classifier…”