ReviewIranian journal of public health2017
Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review.
Review in Iranian journal of public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- CPSM: an R package for cancer patient survival risk model using transcriptomics and clinical data.GigaScience · 2026Article
- AHDSN: an attention-enabled hybrid deep sequential network for cancer survivability prediction from multi-omics data.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Article
- Application of Machine Learning for Predicting Progression-Free and Overall Survival in Patients With Renal Cell Carcinoma.Clinical and translational science · 2025Article
- Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors.Alzheimer's research & therapy · 2024Article
- Review
- Predicting long-term outcomes of kidney transplantation in the era of artificial intelligence.Scientific reports · 2023Article
- Comparison of wavelet transformations to enhance convolutional neural network performance in brain tumor segmentation.BMC medical informatics and decision making · 2021Article
- Review
- A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning.Entropy (Basel, Switzerland) · 2021Article
- Pancreatic Cancer Survival Prediction: A Survey of the State-of-the-Art.Computational and mathematical methods in medicine · 2021Review
- miR-497-5p Decreased Expression Associated with High-Risk Endometrial Cancer.International journal of molecular sciences · 2020Article
- [Otolaryngology head and neck surgery in the age of artificial intelligence].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2020Review
- Learning promotion of physiotherapy in neurological diseases: Design and application of a virtual reality-based game.Journal of education and health promotion · 2020Article
- Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.PloS one · 2020Article
- Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis.JMIR medical informatics · 2019Article
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.Human genetics · 2019Review
- Association of specific gene mutations derived from machine learning with survival in lung adenocarcinoma.PloS one · 2018Article
- Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes.International journal of genomics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundToday, 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.
methodsThis 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.
resultsStudies 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.
conclusionBy 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.
Indexed as
Identifiers
28451550PMC5402773W2594496545What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.