Northampton Electronic Collection of Theses and Research

A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction

Khalsan, M., Machado, L., SALIH AL-SHAMERY, E., Ajit, S., Anthony, K., Mu, M. and Opoku Agyeman, M. (2022) A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction. IEEE Access. 2169-3536.

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Khalsan_etal_IEEEAccess_2022_A_Survey_of_Machine_Learning_Approaches_Applied_to_Gene_Expression_Analysis_for_Cancer_Prediction
Khalsan_etal_IEEEAccess_2022_A_Survey_of_Machine_Learning_Approaches_Applied_to_Gene_Expression_Analysis_for_Cancer_Prediction.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
16549:35832
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Jasim_etal_IEEEAccess_2022_A_Survey_of_Machine_Learning_Approaches_Applied_to_Gene_Expression_Analysis_for_Cancer_Prediction
Jasim_etal_IEEEAccess_2022_A_Survey_of_Machine_Learning_Approaches_Applied_to_Gene_Expression_Analysis_for_Cancer_Prediction.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Information
Abstract:
Machine learning approaches are powerful techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. Our survey provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (breast, lung, kidney, ovarian, liver, central nervous system and gallbladder) for survival prediction,tumor identification and stratification. We also provide an overview of biomarker studies that are associated with these cancer types. The survey captures multiple aspects of machine learning associated cancer studies,including cancer classification, cancer prediction, identification of biomarker genes, microarray, and RNA-Seq data.We discuss the technical issues with current cancer prediction models and the corresponding measurement tools for determining the activity levels of gene expression between cancerous tissues and noncancerous tissues. Additionally, we investigate how identifying putative biomarker gene expression patterns can aid in predicting future risk of cancer and inform the provision of personalized treatment.
Uncontrolled Keywords:
/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Creators:
Khalsan, M., Machado, L., SALIH AL-SHAMERY, E., Ajit, S., Anthony, K., Mu, M. and Opoku Agyeman, M.
Faculties, Divisions and Institutes:
?? ASSCI ??
Research Centres > Centre for Physical Activity and Life Sciences
Faculties > Faculty of Health & Society > Sports, Exercise & Life Sciences
University Faculties, Divisions and Research Centres - OLD > Research Group > Science and Technology Research in Pedagogy
Research Centres > Science and Technology Research in Pedagogy
Faculties > Faculty of Arts, Science & Technology > Computing
Research Centres > Northamptonshire Dementia Research & Innovation Centre
Faculties > Faculty of Arts, Science & Technology
Date:
18 March 2022
Date Type:
Publication
Journal or Publication Title:
IEEE Access
Language:
English
DOI:
10.1109/ACCESS.2022.3146312
ISSN:
2169-3536
Status:
Published / Disseminated
Refereed:
Yes
Related URLs:
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