Deep Neural Network Based Precursor microRNA Prediction on Eleven Species

2017/04/10 by Thomas, Jaya, Sael, Lee
#FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Quantitative Methods (q-bio.QM)

paper · doi:10.48550/arxiv.1704.03834

Abstract

MicroRNA (miRNA) are small non-coding RNAs that regulates the gene expression at the post-transcriptional level. Determining whether a sequence segment is miRNA is experimentally challenging. Also, experimental results are sensitive to the experimental environment. These limitations inspire the development of computational methods for predicting the miRNAs. We propose a deep learning based classification model, called DP-miRNA, for predicting precursor miRNA sequence that contains the miRNA sequence. The feature set based Restricted Boltzmann Machine method, which we call DP-miRNA, uses 58 features that are categorized into four groups: sequence features, folding measures, stem-loop features and statistical feature. We evaluate the performance of the DP-miRNA on eleven twelve data sets of varying species, including the human. The deep neural network based classification outperformed support vector machine, neural network, naive Baye's classifiers, k-nearest neighbors, random forests, and a hybrid system combining support vector machine and genetic algorithm.

Related

• deepMiRGene: Deep Neural Network based Precursor microRNA Prediction
• Effective Classification of MicroRNA Precursors Using Combinatorial Feature Mining and AdaBoost Algorithms
• Feature versus Raw Sequence: Deep Learning Comparative Study on Predicting Pre-miRNA
• deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks
• miGAP: miRNA–Gene Association Prediction Method Based on Deep Learning Model