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Exploration of Pote...
Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence
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- Hamidi, Farzaneh (författare)
- Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran, Islamic R.
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- Gilani, Neda (författare)
- Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran, Islamic R.
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- Belaghi, Reza Arabi (författare)
- Uppsala universitet,Tillämpad matematik och statistik,Univ Tabriz, Fac Math Sci, Dept Stat, Tabriz, Iran, Islamic R
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- Sarbakhsh, Parvin (författare)
- Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran, Islamic R.
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- Edgünlü, Tuba (författare)
- Mugla Sitki Kocman Univ, Fac Med, Dept Med Biol, Mugla, Turkey.
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- Santaguida, Pasqualina (författare)
- McMaster Univ, Dept Hlth Res & Methods, Hamilton, ON, Canada.
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Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran, Islamic R Tillämpad matematik och statistik (creator_code:org_t)
- 2021-11-25
- 2021
- Engelska.
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Ingår i: Frontiers in Genetics. - : Frontiers Media S.A.. - 1664-8021. ; 12
- Relaterad länk:
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https://doi.org/10.3...
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https://uu.diva-port... (primary) (Raw object)
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https://www.frontier...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- Ovarian cancer is the second most dangerous gynecologic cancer with a high mortality rate. The classification of gene expression data from high-dimensional and small-sample gene expression data is a challenging task. The discovery of miRNAs, a small non-coding RNA with 18-25 nucleotides in length that regulates gene expression, has revealed the existence of a new array for regulation of genes and has been reported as playing a serious role in cancer. By using LASSO and Elastic Net as embedded algorithms of feature selection techniques, the present study identified 10 miRNAs that were regulated in ovarian serum cancer samples compared to non-cancer samples in public available dataset GSE106817: hsa-miR-5100, hsa-miR-6800-5p, hsa-miR-1233-5p, hsa-miR-4532, hsa-miR-4783-3p, hsa-miR-4787-3p, hsa-miR-1228-5p, hsa-miR-1290, hsa-miR-3184-5p, and hsa-miR-320b. Further, we implemented state-of-the-art machine learning classifiers, such as logistic regression, random forest, artificial neural network, XGBoost, and decision trees to build clinical prediction models. Next, the diagnostic performance of these models with identified miRNAs was evaluated in the internal (GSE106817) and external validation dataset (GSE113486) by ROC analysis. The results showed that first four prediction models consistently yielded an AUC of 100%. Our findings provide significant evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinsk bioteknologi -- Medicinsk bioteknologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Medical Biotechnology -- Medical Biotechnology (hsv//eng)
Nyckelord
- Biomarker
- Elasticnet
- Feature Selection
- Gene Expression Omnibus (GEO)
- Lasso
- Machine Learning
- Ovarian Cancer
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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