Gene Selection and Cancer Classification Using a Multidimensional Fuzzy Deep Learning Approach for Gene Expression Data
type: CreativeWork
description: Deep Learning approaches are powerful techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. This thesis provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (i.e Breast, Lung, Kidney, Liver, Gallbladder, Gastric, and Thyroid) for survival prediction, tumour identification, and stratification as well as providing an overview of biomarker studies that are associated with these cancer types. The thesis captures multiple aspects of machine learning-associated cancer studies, including cancer classification, cancer prediction, identification of biomarker genes, microarray, and RNA-Seq data. The thesis discussed the technical issues with current cancer classification models and the corresponding measurement tools for determining the activity levels of gene expression between cancerous tissues and noncancerous tissues. The work has not only highlighted the issues but also attempted to address the issues that arise in the previous studies. One of the notable issues that employ gene expression data for cancer classification is the high dimensionality of the available datasets. As a result, the work developed a fusion three feature selection initial approach to reduce the number of genes and increase the accuracy of cancer classification by using the concept of intersection. fusion three feature selection method was developed to select an optimal subset of genes that would be used as identifiers for classification and reduce the dimensionality of the available data in gene expression. The results ratings for employed cancer datasets ranged from (95.5% to 98%) accuracy, (94.4% to 100%) precision, (94% to 100%) recall, and (95.7% to 98%) f1-score. Therefore, a new fuzzy gene selection approach was developed to identify significant genes to facilitate cancer classification and reduce the dimensionality of the available gene expression data. Thi…
abstract: Deep Learning approaches are powerful techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. This thesis provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (i.e Breast, Lung, Kidney, Liver, Gallbladder, Gastric, and Thyroid) for survival prediction, tumour identification, and stratification as well as providing an overview of biomarker studies that are associated with these cancer types. The thesis captures multiple aspects of machine learning-associated cancer studies, including cancer classification, cancer prediction, identification of biomarker genes, microarray, and RNA-Seq data. The thesis discussed the technical issues with current cancer classification models and the corresponding measurement tools for determining the activity levels of gene expression between cancerous tissues and noncancerous tissues. The work has not only highlighted the issues but also attempted to address the issues that arise in the previous studies. One of the notable issues that employ gene expression data for cancer classification is the high dimensionality of the available datasets. As a result, the work developed a fusion three feature selection initial approach to reduce the number of genes and increase the accuracy of cancer classification by using the concept of intersection. fusion three feature selection method was developed to select an optimal subset of genes that would be used as identifiers for classification and reduce the dimensionality of the available data in gene expression. The results ratings for employed cancer datasets ranged from (95.5% to 98%) accuracy, (94.4% to 100%) precision, (94% to 100%) recall, and (95.7% to 98%) f1-score. Therefore, a new fuzzy gene selection approach was developed to identify significant genes to facilitate cancer classification and reduce the dimensionality of the available gene expression data. Thi…
author: Khalsan, Mahmood
language: eng
keywords: Gene Selection, Deep learning, Cancer Classification
date published: 2024-4-4
date modified: 2026-07-24T03:26:59Z
provider: University of Northampton
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Northampton