کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
2820987 1160913 2012 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
MiRANN: A reliable approach for improved classification of precursor microRNA using Artificial Neural Network model
موضوعات مرتبط
علوم زیستی و بیوفناوری بیوشیمی، ژنتیک و زیست شناسی مولکولی ژنتیک
پیش نمایش صفحه اول مقاله
MiRANN: A reliable approach for improved classification of precursor microRNA using Artificial Neural Network model
چکیده انگلیسی

MicroRNA (miRNA) is a special class of short noncoding RNA that serves pivotal function of regulating gene expression. The computational prediction of new miRNA candidates involves various methods such as learning methods and methods using expression data. This article has proposed a reliable model — miRANN which is a supervised machine learning approach. MiRANN used known pre-miRNAs as positive set and a novel negative set from human CDS regions. The number of known miRNAs is now huge and diversified that could cover almost all characteristics of unknown miRNAs which increases the quality of the result (99.9% accuracy, 99.8% sensitivity, 100% specificity) and provides a more reliable prediction. MiRANN performs better than other state-of-the-art approaches and declares to be the most potential tool to predict novel miRNAs. We have also tested our result using a previous negative set. MiRANN, opens new ground using ANN for predicting pre-miRNAs with a promise of better performance.


► MiRANN: a reliable approach for improved classification of precursor microRNA.
► We used Artificial Neural Network as a machine learning algorithm.
► MiRANN is trained with human data and gives 99.9% ACC, 99.8% SE, 100% SP.
► MiRANN has outperformed other state-of-the-art approaches with higher accuracy.
► MiRANN is tested with different datasets.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Genomics - Volume 99, Issue 4, April 2012, Pages 189–194
نویسندگان
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