Kernelized Bayesian Matris Faktorizasyonu ile mikroRNA-Hastalık İlişkilerinin Tanımlanması ve Analizi

Year 2021, Issue: 28, 40 - 45, 30.11.2021

Ahmet Toprak , Esma Eryılmaz Doğan

https://doi.org/10.31590/ejosat.980257

Abstract

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.

Keywords

miRNA, hastalık, miRNA-hastalık ilişkisi, benzerlik ölçümü

Identification and Analysis of microRNA-Disease Associations with Kernelized Bayesian Matrix Factorization

Abstract

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.

Keywords

miRNA, disease, miRNA-Disease Association, similarity measure

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