Makine Öğrenmesi Tabanlı Mikrodizi Tekniği ile MikroRNA Hedef Tahmini: Araştırma Çalışması

Biyoenformatik, biyolojik bilgilerin bilgisayar teknolojileri yardımıyla incelenmesini ve değerlendirilmesini sağlayan bir araştırma alanıdır. Çok disiplinli bu alan sayesinde tıbbi veriler üzerinde yapılan çalışmalarda hızla yol alınabilmekte, gerek hastalıkların teşhis-tedavi süreçlerinde gerek önlenmesi süreçlerinde başarılı çözümler bulunabilmektedir. Birçok farklı organizmada görülen ve hücre üzerinde olaylarda etkili olduğu ortaya çıkan mikroRNA (miRNA, miR olarak da isimlendirilir, mikro RiboNükleik Asit’in kısaltmasıdır)’ların genler üzerindeki etkisi ile ilgili çalışmalar da biyoenformatik yöntemler yardımıyla başarılı sonuçlar vermektedir. Özellikle kanser ile yakın ilişkili olduğu düşünülen mikroRNA’ların incelenmesinde mikrodizi teknikleri sıklıkla tercih edilmektedir. Mikrodizi olarak hazırlanan veri setleri makine öğrenmesi yöntemleri ile değerlendirilerek mikroRNA hedef genlerinin belirlenmesi, mikroRNA’ya bağlı hastalık/kanserin teşhis ve tedavi süreçleri ile ilgili hızlı ve doğruluğu yüksek sonuçlar elde edilebilmektedir. Bu araştırma çalışmasında, mikroRNA hedef gen tahmini sürecinde makine öğrenmesi tekniklerinin kullanımı incelenmiştir.

MicroRNA Target Prediction by Machine Learning-Based Microarray Technique: Research Study

Bioinformatics is a research field that enables the examination and evaluation of biological information with the help of computer technologies. With the help of this multidisciplinary field, studies on medical data can progress rapidly, and successful solutions can be found both in the diagnosis-treatment processes of diseases and in the prevention processes. Studies on the effects of microRNAs (miRNA, also called miR, an abbreviation for micro RiboNucleic Acid) that are seen in many different organisms and are effective in events on the cell, also give successful results with the help of bioinformatics methods. Microarray techniques are frequently preferred especially in the examination of microRNAs that are thought to be closely related to cancer. By evaluating the data sets prepared as microarrays with machine learning methods, fast and high-accuracy results can be obtained regarding the determination of microRNA target genes, diagnosis and treatment processes of microRNA-related disease/cancer., In this research study, the use of machine learning techniques in the microRNA target gene prediction process was examined.

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