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Prediction of Potential MicroRNA–Disease Association Using Kernelized Bayesian Matrix Factorization

Interdisciplinary Sciences – Computational Life Sciences · Aralık 2021

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YÖKSİS Kayıtları
Prediction of Potential MicroRNA-Disease Association Using Kernelized Bayesian Matrix Factorization
Interdisciplinary Sciences: Computational Life Sciences · 2021 SCI-Expanded
DOÇENT AHMET TOPRAK →
Prediction of Potential MicroRNA-Disease Association Using Kernelized Bayesian Matrix Factorization
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES · 2021 SCI
DOÇENT ESMA ERYILMAZ DOĞAN →
Prediction of Potential MicroRNA-Disease Association Using Kernelized Bayesian Matrix Factorization
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES · 2021 SCI-Expanded
DOÇENT AHMET TOPRAK →

Makale Bilgileri

DergiInterdisciplinary Sciences – Computational Life Sciences
Yayın TarihiAralık 2021
Cilt / Sayfa13 · 595-602
DOI10.1007/s12539-021-00469-w
Scopus ID2-s2.0-85112424448
Özet MicroRNA (miRNA) molecules, which are effective in the formation and progression of many different diseases, are 18–22 nucleotides in length and make up a type of non-coding RNA. Predicting disease-related microRNAs is crucial for understanding the pathogenesis of disease and for diagnosis, treatment, and prevention of diseases. Many computational techniques have been studied and developed, as the experimental techniques used to find novel miRNA–disease associations in biology are costly. In this paper, a Kernelized Bayesian Matrix Factorization (KBMF) technique was suggested to predict new relations among miRNAs and diseases with several information such as miRNA functional similarity, disease semantic similarity, and known relations among miRNAs and diseases. AUC value of 0.9450 was obtained by implementing fivefold cross-validation for KBMF technique. We also carried out three kinds of case studies (breast, lung, and colon neoplasms) to prove the performance of KBMF technique, and the predictive reliability of this method was confirmed by the results. Thus, KBMF technique can be used as a reliable computational model to infer possible miRNA–disease associations.

Yazarlar (2)

1
Ahmet Toprak
ORCID: 0000-0003-3337-4917
2
Esma Eryilmaz Dogan

Anahtar Kelimeler

Disease miRNA miRNA–disease relationship Similarity measure

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey

Metrikler

3
Atıf
2
Yazar
4
Anahtar Kelime

Sistemimizdeki Yazarlar

EE
ESMA ERYILMAZ DOĞAN
DOÇENT

Hızlı Erişim

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