Corpus ID: 3261635

Deep Neural Network Based Precursor microRNA Prediction on Eleven Species

  title={Deep Neural Network Based Precursor microRNA Prediction on Eleven Species},
  author={Jaya Thomas and Lee Sael},
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… Expand
Predicting novel microRNA: a comprehensive comparison of machine learning approaches
This review provides a comprehensive study and comparative assessment of methods from these two ML approaches for dealing with the prediction of novel pre-miRNAs: supervised and unsupervised training and suggests that from low to mid-imbalance levels between classes, supervised methods can be the best. Expand
Genome-wide discovery of pre-miRNAs: comparison of recent approaches based on machine learning
Six recent methods for tackling the genome-wide discovery of microRNAs (miRNAs) by identifying sequences having the highest chance of being a novel miRNA precursor (pre-miRNA), within all the possible sequences in a complete genome are reviewed. Expand
An efficient gene bigdata analysis using machine learning algorithms
Experimental results indicate that machine learning algorithms certainly increases the efficiency of Bioinformatics-based methods of processing gene data in terms of prediction accuracy and reduced processing time. Expand


DP-miRNA: An improved prediction of precursor microRNA using deep learning model
The deep neural network based classification model for predicting precursor miRNA sequence that contains the mi RNA sequence outperformed support vector machine, neural network, naive Bayes classifiers, k-nearest neighbors, random forests as well as hybrid systems combining SVM and genetic algorithm. Expand
Predicting human microRNA precursors based on an optimized feature subset generated by GA-SVM.
An optimized feature subset including 13 features using a hybrid of genetic algorithm and support vector machine (GA-SVM) is generated and miR-SF is effective for identifying pre-miRNAs. Expand
MiRANN: a reliable approach for improved classification of precursor microRNA using Artificial Neural Network model.
A reliable model is proposed - miRANN which is a supervised machine learning approach that performs better than other state-of-the-art approaches and declares to be the most potential tool to predict novel miRNAs. Expand
Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine
The local structure-sequence features reflect discriminative and conserved characteristics of miRNAs, and the successful ab initio classification of real and pseudo pre-miRNAs opens a new approach for discovering new mi RNAs. Expand
MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans
A computational learning method SVM (support vector machine) was implemented to build a high throughput and good performance computational pre-miRNA prediction tool called MiRFinder, designed for genome-wise, pair-wise sequences from two related species. Expand
MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features
Aiming at better prediction performance, an ensemble support vector machine (SVM) classifier is established to deal with the imbalance issue, and multi-loop features are included for identifying those pre-miRNAs with multi-loops. Expand
De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures
MOTIVATION MicroRNAs (miRNAs) are small ncRNAs participating in diverse cellular and physiological processes through the post-transcriptional gene regulatory pathway. Critically associated with theExpand
Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm
The proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set, and can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Expand
microPred: effective classification of pre-miRNAs for human miRNA gene prediction
The development of an effective classifier system (named as microPred) for this classification problem by using appropriate machine learning techniques and extensive classifier performance evaluation via systematic cross-validation methods is presented. Expand
Identification of microRNA precursors based on random forest with network-level representation method of stem-loop structure
Network parameters effectively characterize pre-miRNA secondary structure, which improves the prediction model in both prediction ability and computation efficiency and can reflect natural properties of miRNAs and be used for comprehensive and systematic analysis on miRNA. Expand