Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review
Background: Today, despite the many advances in early detection of diseases, cancer patients have a poor prognosis and the survival rates in them are low. Recently, microarray technologies have been used for gathering thousands data about the gene expression level of cancer cells. These types of dat...
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| Format: | Article |
| Language: | English |
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Tehran University of Medical Sciences
2017-02-01
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| Series: | Iranian Journal of Public Health |
| Subjects: | |
| Online Access: | https://ijph.tums.ac.ir/index.php/ijph/article/view/9044 |
| _version_ | 1818920019708346368 |
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| author | Azadeh BASHIRI Marjan GHAZISAEEDI Reza SAFDARI Leila SHAHMORADI Hamide EHTESHAM |
| author_facet | Azadeh BASHIRI Marjan GHAZISAEEDI Reza SAFDARI Leila SHAHMORADI Hamide EHTESHAM |
| author_sort | Azadeh BASHIRI |
| collection | DOAJ |
| description | Background: Today, despite the many advances in early detection of diseases, cancer patients have a poor prognosis and the survival rates in them are low. Recently, microarray technologies have been used for gathering thousands data about the gene expression level of cancer cells. These types of data are the main indicators in survival prediction of cancer. This study highlights the improvement of survival prediction based on gene expression data by using machine learning techniques in cancer patients.
Methods: This review article was conducted by searching articles between 2000 to 2016 in scientific databases and e-Journals. We used keywords such as machine learning, gene expression data, survival and cancer.
Results: Studies have shown the high accuracy and effectiveness of gene expression data in comparison with clinical data in survival prediction. Because of bewildering and high volume of such data, studies have highlighted the importance of machine learning algorithms such as Artificial Neural Networks (ANN) to find out the distinctive signatures of gene expression in cancer patients. These algorithms improve the efficiency of probing and analyzing gene expression in cancer profiles for survival prediction of cancer.
Conclusion: By attention to the capabilities of machine learning techniques in proteomics and genomics applications, developing clinical decision support systems based on these methods for analyzing gene expression data can prevent potential errors in survival estimation, provide appropriate and individualized treatments to patients and improve the prognosis of cancers. |
| first_indexed | 2024-12-20T01:15:06Z |
| format | Article |
| id | doaj.art-9dbd2eaeea70432f970d01da843ebbb0 |
| institution | Directory Open Access Journal |
| issn | 2251-6085 2251-6093 |
| language | English |
| last_indexed | 2024-12-20T01:15:06Z |
| publishDate | 2017-02-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Iranian Journal of Public Health |
| spelling | doaj.art-9dbd2eaeea70432f970d01da843ebbb02022-12-21T19:58:36ZengTehran University of Medical SciencesIranian Journal of Public Health2251-60852251-60932017-02-01462Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative ReviewAzadeh BASHIRI0Marjan GHAZISAEEDI1Reza SAFDARI2Leila SHAHMORADI3Hamide EHTESHAM4Dept. of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, IranDept. of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, IranDept. of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, IranDept. of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, IranDept. of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, IranBackground: Today, despite the many advances in early detection of diseases, cancer patients have a poor prognosis and the survival rates in them are low. Recently, microarray technologies have been used for gathering thousands data about the gene expression level of cancer cells. These types of data are the main indicators in survival prediction of cancer. This study highlights the improvement of survival prediction based on gene expression data by using machine learning techniques in cancer patients. Methods: This review article was conducted by searching articles between 2000 to 2016 in scientific databases and e-Journals. We used keywords such as machine learning, gene expression data, survival and cancer. Results: Studies have shown the high accuracy and effectiveness of gene expression data in comparison with clinical data in survival prediction. Because of bewildering and high volume of such data, studies have highlighted the importance of machine learning algorithms such as Artificial Neural Networks (ANN) to find out the distinctive signatures of gene expression in cancer patients. These algorithms improve the efficiency of probing and analyzing gene expression in cancer profiles for survival prediction of cancer. Conclusion: By attention to the capabilities of machine learning techniques in proteomics and genomics applications, developing clinical decision support systems based on these methods for analyzing gene expression data can prevent potential errors in survival estimation, provide appropriate and individualized treatments to patients and improve the prognosis of cancers.https://ijph.tums.ac.ir/index.php/ijph/article/view/9044SurvivalCancerGene expressionMachine-learning techniquesClinical decision support system |
| spellingShingle | Azadeh BASHIRI Marjan GHAZISAEEDI Reza SAFDARI Leila SHAHMORADI Hamide EHTESHAM Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review Iranian Journal of Public Health Survival Cancer Gene expression Machine-learning techniques Clinical decision support system |
| title | Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review |
| title_full | Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review |
| title_fullStr | Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review |
| title_full_unstemmed | Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review |
| title_short | Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review |
| title_sort | improving the prediction of survival in cancer patients by using machine learning techniques experience of gene expression data a narrative review |
| topic | Survival Cancer Gene expression Machine-learning techniques Clinical decision support system |
| url | https://ijph.tums.ac.ir/index.php/ijph/article/view/9044 |
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