Optimized gene selection and classification of cancer from microarray gene expression data using deep learning
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S.I. : BIO-INSPIRED COMPUTING FOR DLA
Optimized gene selection and classification of cancer from microarray gene expression data using deep learning Shamveel Hussain Shah1 • Muhammad Javed Iqbal1 • Iftikhar Ahmad2 Joel J. P. C. Rodrigues4,5
•
Suleman Khan3
•
Received: 5 May 2020 / Accepted: 16 September 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Cancer is the major leading reason of death around the world. However, the early identification and prediction of a cancer type is very critical for patient’s health. Recently, microarray gene expression data was utilized for efficient and early diagnosis of cancer. Previous work shows that microarray data has two major issues which are high dimensionality and small sample size. Several researchers have analyzed and evaluated the cancer classification problem using different statistical and machine learning-based approaches but there are still some issues with these approaches that make cancer classification a nontrivial task. Such as, the inability of certain machine learning algorithms to use unstructured data has limited their utility in the cancer classification process. Convolutional neural networks are proven to very suitable to analyze variety of unstructured data. This ability allowed the deep learning algorithms to play a vibrant part in early detection of cancer through data classification. In this research, a hybrid deep learning model based on Laplacian ScoreConvolutional Neural Network (LS-CNN) is employed for the classification of given cancer’s data. The performance of the proposed system was evaluated on 10 different benchmark datasets using various performance measurement metrics such as accuracy and confusion matrix. The experimental results conclude that proposed LS-CNN model outperformed compared to traditional machine learning and recently used deep learning approaches. Keywords Microarray data Deep learning Laplacian score (LS) Convolutional neural network (CNN)
1 Introduction Modern deep learning-based techniques has proven to very successful in dealing variety of structure and unstructured data comprising of image, audio, video, text and disease related data. In cancer disease, cells in some tissues
& Iftikhar Ahmad [email protected] 1
Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
2
Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
3
Department of Computer and Information Sciences, Northumbria University, Newcastle NE1 8ST, UK
4
Federal University of Piauı´ (UFPI), Teresina, PI, Brazil
5
Instituto de Telecomunicac¸o˜es, Covilha˜, Portugal
undergo uncontrolled division in the body. Because of this condition, malignant growth occurs in the body and cancer effected cells destroy neighbor’s healthy tissues and organs. According to National Cancer Institute (NCI) report currently there are more than 200 different cancer types [1
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