A Comparative Study of Gene Selection Methods for Microarray Cancer Classification

Alshamlan, Hala; Badr, Ghada; Al-Ohali, Yousef

doi:10.1007/978-981-13-1799-6_60

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Enter the era of powerful computing, and it saw the massive upsurge in the field of Deep Learning approaches, which efficiently tackled unstructured higher dimensional data with ease and provided an astounding accuracy whenever applied to any field of study. As such a plethora of Deep Learning work has summoned the door of dealing with the high dimension of microarray data and derived output classification based on that, but still they bear that ideology of transformation to some lower order variant via previous dimensionality reduction algorithms [3][4][5][6][7], Heatmaps [8], Statistical inferences [9], etc. and doesn't maximize the full utility of neural networks which are some excellent feature extractors.…”

Section: Introductionmentioning

confidence: 99%

Feature Mining and Classification of Microarray Data Using Modified ResNet-SVM Architecture

Bej¹,

Roy²,

Suchinta³

et al. 2021

Computational Intelligence in Pattern Recognition

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Microarray data have been one of the torch-bearing approaches when ushered for prediction and diagnosis of deadly diseases. But, much of its potential has been overshadowed by its structural orientation of low sample and high dimension which have provided the dilemma for researchers working in this field of study. In recent times, many approaches have been adopted to tackle this dilemma through feature extraction and thereafter classification that have stapled very concrete results. But, the methodologies lacked direct implementation and required the transformation of the data into a relevant intermediate matrix, before being mobilized for feature extraction and classification. In this paper, an effective hybrid alternative has been proposed which utilizes a modified single-dimensional (1D) Residual Network (ResNet) for lucid feature extraction directly from the gathered datasets and a Support Vector Machine (SVM) classifier for diseases classification. The proposed methodology is tested on three datasets (namely Colon cancer, Leukemia, and Lung cancer) and derived a staggering classification accuracy.

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