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.
Khalsan_etal_IEEEAccess_2022_A_Survey_of_Machine_Learning_Approach ... (3MB) |
Jasim_etal_IEEEAccess_2022_A_Survey_of_Machine_Learning_Approaches ... (4MB) |
| Item Type: | Article |
|---|---|
| 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, Mahmood, Machado, Lee, SALIH AL-SHAMERY, EMAN, Ajit, Suraj, Anthony, Karen, Mu, Mu and Opoku Agyeman, Michael |
| 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: | https://doi.org/10.1109/ACCESS.2022.3146312 |
| ISSN: | 2169-3536 |
| Status: | Published / Disseminated |
| Refereed: | Yes |
| Related URLs: | |
| URI: | http://nectar.northampton.ac.uk/id/eprint/16549 |
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