TY  - JOUR
T1  - Identification and Analysis of microRNA-Disease Associations with Kernelized Bayesian Matrix Factorization
TT  - Kernelized Bayesian Matris Faktorizasyonu ile mikroRNA-Hastalık İlişkilerinin Tanımlanması ve Analizi
AU  - Toprak, Ahmet
AU  - Eryılmaz Doğan, Esma
PY  - 2021
DA  - November
DO  - 10.31590/ejosat.980257
JF  - Avrupa Bilim ve Teknoloji Dergisi
JO  - EJOSAT
PB  - Osman SAĞDIÇ
WT  - DergiPark
SN  - 2148-2683
SP  - 40
EP  - 45
IS  - 28
LA  - en
AB  - MicroRNA (miRNA) molecules, which are effective on the initiation and progression of many different diseases, are a type of non-coding RNA with a length of about 22 nucleotides. Scientists have reported the importance of miRNAs in the prevention, diagnosis, and treatment of complex human diseases. Therefore, in the last decade, researchers have been working hard to find potential miRNA-disease associations. Many computational techniques have been developed because of the experimental techniques are time-consuming and expensive used to find new relationships between miRNAs and diseases. In this study, we suggested Kernelized Bayesian matrix factorization (KBMF) technique to predict new miRNA-disease relationships. We applied 5-fold cross validation technique and obtained an average value AUC of 0.9450. Also, we applied case studies based on breast, lung, and colon neoplasms to prove the performance of KBMF technique. The results showed that KBMF can be used as a reliable computational model to reveal possible miRNA-disease relationships.
KW  - miRNA
KW  - disease
KW  - miRNA-Disease Association
KW  - similarity measure
N2  - Birçok farklı hastalığın başlamasında ve ilerlemesinde etkili olan mikroRNA (miRNA) molekülleri yaklaşık 22 nükleotid uzunluğunda kodla yapmayan bir RNA türüdür. Bilim insanları karmaşık insan hastalıklarının önlenmesi, teşhisi ve tedavisinde miRNA’ların önemini açıklamıştır. Bu nedenle son yıllarda araştırmacılar potansiyel miRNA-hastalık ilişkilerini bulmak için çok çalışmaktalar. miRNA’lar ve hastalıklar arasında yeni ilişkiler bulmak için kullanılan deneysel tekniklerin zaman alıcı ve pahalı olması nedeniyle birçok hesaplama tekniği geliştirilmiştir. Bu çalışmada yeni miRNA-hastalık ilişkilerini tahmin etmek için Kernelized Bayesian matrix factorization (KBMF) tekniğini önerdik. 5-katlı çapraz doğrulama tekniği uyguladık ve 0.9450 ortalama AUC değeri elde ettik. Ayrıca KBMF tekniğinin performansını kanıtlamak için meme, akciğer ve kolon neoplazmalarına dayalı vaka çalışmaları uyguladık. Sonuçlar KBMF’nin olası miRNA-hastalık ilişkilerini ortaya çıkarmak için güvenilir bir hesaplama modeli olarak kullanılabileceğini gösterdi.
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UR  - https://doi.org/10.31590/ejosat.980257
L1  - https://dergipark.org.tr/tr/download/article-file/1914521
ER  -