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mirLSTM: A Deep Sequential Approach to MicroRNA Target Binding Site Prediction

  • Ahmet PakerEmail author
  • Hasan OğulEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1062)

Abstract

MicroRNAs (miRNAs) are small and non-coding RNAs of ~21–23 base length, which play critical role in gene expression. They bind the target mRNAs in the post-transcriptional level and cause translational inhibition or mRNA cleavage. Quick and effective detection of the binding sites of miRNAs is a major problem in bioinformatics. In this study, a deep learning approach based on Long Short Term Memory (LSTM) is developed with the help of an existing duplex sequence model. Compared with four conventional machine learning methods, the proposed LSTM model performs better in terms of the accuracy (ACC), sensitivity, specificity, AUC (Area under the curve) and F1 score. A web-tool is also developed to identify and display the microRNA target sites effectively and quickly.

Keywords

Deep learning RNN LSTM Bioinformatics Sequence Alignment miRNA Target prediction miRNA target site 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer EngineeringBaşkent UniversityAnkaraTurkey
  2. 2.Faculty of Computer SciencesØstfold University CollegeHaldenNorway

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