Identifying miRNA biomarkers for breast cancer and ovarian cancer: a text mining perspective.

Li, Xin; Dai, Andrea; Tran, Richard; Wang, Jie

Li, Xin; Dai, Andrea; Tran, Richard; Wang, Jie.

Affiliation

Li X; Ophthalmology Department, Central Hospital Affiliated to Shandong First Medical University, Jinan, 250013, Shandong, China.
Dai A; Oakland University William Beaumont School of Medicine, Rochester, MI, 48309, USA.
Tran R; Masters Program in Computer Science, University of Chicago, Chicago, IL, 20833, USA.
Wang J; Applied Data Science Program, Syracuse University, Syracuse, NY, 13244, USA. jwang326@syr.edu.

Breast Cancer Res Treat ; 201(1): 5-14, 2023 Aug.

Article in En | MEDLINE | ID: mdl-37329459

Subject(s)

Breast Neoplasms; MicroRNAs; Ovarian Neoplasms; Humans; Female; MicroRNAs/genetics; Breast Neoplasms/diagnosis; Breast Neoplasms/genetics; Bayes Theorem; Biomarkers, Tumor/genetics; Ovarian Neoplasms/diagnosis; Ovarian Neoplasms/genetics; Gene Expression Regulation, Neoplastic; Gene Expression Profiling

Key words

Breast cancer; Machine learning; Ovarian cancer; Prediction model; miR-155; miR-182; microRNAs

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Ovarian Neoplasms / Breast Neoplasms / MicroRNAs Type of study: Diagnostic_studies / Prognostic_studies Limits: Female / Humans Language: En Journal: Breast Cancer Res Treat Year: 2023 Document type: Article Affiliation country: China

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