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Identifying potenti...
Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach : application of Boruta
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- Hamidi, Farzaneh (författare)
- Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran.
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- Gilani, Neda (författare)
- Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran.;Tabriz Univ Med Sci, Rd Traff Injury Res Ctr, Tabriz, Iran.
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- Belaghi, Reza (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Uppsala universitet,Statistik, AI och data science,Univ Tabriz, Fac Math Sci, Dept Stat, Tabriz, Iran.;Swedish Agr Univ, Dept Energy & Technol, Uppsala, Sweden.,Institutionen för energi och teknik,Department of Energy and Technology,Uppsala University,University of Tabriz
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- Yaghoobi, Hanif (författare)
- Univ Tabriz, Sch Nat Sci, Dept Biol Sci, Tabriz, Iran.
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- Babaei, Esmaeil (författare)
- Univ Tabriz, Sch Nat Sci, Dept Biol Sci, Tabriz, Iran.;Univ Tubingen, Interfac Inst Bioinformat & Med Informat IBMI, Tubingen, Germany.
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- Sarbakhsh, Parvin (författare)
- Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran.
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- Malakouti, Jamileh (författare)
- Tabriz Univ Med Sci, Fac Nursing & Midwifery, Dept Midwifery, Tabriz, Iran.
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Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran.;Tabriz Univ Med Sci, Rd Traff Injury Res Ctr, Tabriz, Iran. (creator_code:org_t)
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- Frontiers Media S.A. 2023
- 2023
- Engelska.
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Ingår i: FRONTIERS IN DIGITAL HEALTH. - : Frontiers Media S.A.. - 2673-253X. ; 5
- 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://pub.epsilon.... (primary) (Raw object) (free)
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https://urn.kb.se/re...
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https://doi.org/10.3...
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https://res.slu.se/i...
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Abstract
Ämnesord
Stäng
- Introduction: In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult.Methods: By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost.Results: Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- artificial intelligence
- Boruta
- biomarker
- feature selection
- Gene Expression Omnibus
- ovarian cancer
- oncology
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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