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Identifying Potenti...
Identifying Potential miRNA Biomarkers for Gastric Cancer Diagnosis Using Machine Learning Variable Selection Approach
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- Gilani, Neda (författare)
- Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran.
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- Belaghi, Reza Arabi (författare)
- Uppsala universitet,Statistik, AI och data science,Univ Tabriz, Fac Math Sci, Dept Stat, Tabriz, Iran.
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- Aftabi, Younes (författare)
- Tabriz Univ Med Sci, TB & Lung Dis Res Ctr, Tabriz, Iran.
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- Faramarzi, Elnaz (författare)
- Tabriz Univ Med Sci, Liver & Gastrointestinal Dis Res Ctr, Tabriz, Iran.
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- Edguenlue, Tuba (författare)
- Mugla Sitki Kocman Univ, Fac Med, Dept Med Biol, Mugla, Turkey.
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- Somi, Mohammad Hossein (författare)
- Tabriz Univ Med Sci, Liver & Gastrointestinal Dis Res Ctr, Tabriz, Iran.
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Tabriz Univ Med Sci, Fac Hlth, Dept Stat & Epidemiol, Tabriz, Iran Statistik, AI och data science (creator_code:org_t)
- 2022-01-10
- 2022
- 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://doi.org/10.3...
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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
- Aim: This study aimed to accurately identification of potential miRNAs for gastric cancer (GC) diagnosis at the early stages of the disease.Methods: We used GSE106817 data with 2,566 miRNAs to train the machine learning models. We used the Boruta machine learning variable selection approach to identify the strong miRNAs associated with GC in the training sample. We then validated the prediction models in the independent sample GSE113486 data. Finally, an ontological analysis was done on identified miRNAs to eliciting the relevant relationships.Results: Of those 2,874 patients in the training the model, there were 115 (4%) patients with GC. Boruta identified 30 miRNAs as potential biomarkers for GC diagnosis and hsa-miR-1343-3p was at the highest ranking. All of the machine learning algorithms showed that using hsa-miR-1343-3p as a biomarker, GC can be predicted with very high precision (AUC; 100%, sensitivity; 100%, specificity; 100% ROC; 100%, Kappa; 100) using with the cut-off point of 8.2 for hsa-miR-1343-3p. Also, ontological analysis of 30 identified miRNAs approved their strong relationship with cancer associated genes and molecular events.Conclusion: The hsa-miR-1343-3p could be introduced as a valuable target for studies on the GC diagnosis using reliable biomarkers.
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 -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
Nyckelord
- miRNA
- machine learning
- boruta algorithm
- gastric cancer
- hsa-miR-1343-3p
- AUC
- GSE106817
- GSE113486
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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