Elephant Search with Deep Learning for Microarray Data Analysis

TL;DR

This paper introduces a novel elephant search optimization combined with deep learning to improve gene selection and classification accuracy in microarray cancer datasets, addressing high dimensionality challenges.

Contribution

It proposes a new elephant search algorithm for gene selection and integrates it with deep learning for enhanced microarray data classification.

Findings

Outperforms existing methods on nine cancer microarray datasets.

Achieves higher classification accuracy with selected gene features.

Demonstrates effectiveness of combined optimization and deep learning approach.

Abstract

Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, a novel elephant search (ES) based optimization is proposed to select best gene expressions from the large volume of microarray data. Further, a promising machine learning method is envisioned to leverage such high dimensional and complex microarray dataset for extracting hidden patterns inside to make a meaningful prediction and most accurate classification. In particular, stochastic gradient descent based Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on nine most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning (ESDL) approach are compared with most recent published article for its suitability in future Bioinformatics research.