Automatic learning of pre-miRNAs from different species

  title={Automatic learning of pre-miRNAs from different species},
  author={Ivani de Oliveira Negr{\~a}o Lopes and Alexander Schliep and Andr{\'e} Carlos Ponce de Leon Ferreira de Carvalho},
  journal={BMC Bioinformatics},
BackgroundDiscovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data… 
MicroRNA categorization using sequence motifs and k-mers
BackgroundPost-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are
Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures
A novel mutual information-based feature representation algorithm for pre-miRNA sequences and secondary structures, which is capable of catching the interactions between sequence bases and local features of the RNA secondary structure and outperforms others based on both 5-fold cross-validation and the Jackknife test.
Genome-wide pre-miRNA discovery from few labeled examples
MiRNAss, which is a novel method based on semi-supervised learning, takes advantage of the information provided by the unlabeled stem-loops, thereby improving the prediction rates, even when the number of labeled examples is low and not representative of the classes.
A survey of software tools for microRNA discovery and characterization using RNA-seq
A typical miRNA prediction and analysis workflow is described, delineating the objectives, potentialities and main steps of sRNA-seq data analysis projects, from preparatory data processing to mi RNA prediction, quantification and diverse downstream analyses.
Nuevo enfoque de aprendizaje semi-supervisado para la identificación de secuencias en bioinformática
Fil: Yones, Cristian Ariel. Universidad Nacional del Litoral. Facultad de Ingenieria y Ciencias Hidricas; Argentina.
Effects of network topology on the performance of consensus and distributed learning of SVMs using ADMM
The results show that the performance of decentralized ADMM-based learning of SVMs in terms of convergence is improved using graphs with large spectral gaps, higher and homogeneous degrees.


The discriminant power of RNA features for pre-miRNA recognition
This work analyzes the discriminant power of seven feature sets, which are used in six pre-miRNA prediction tools, and proposes a relatively inexpensive feature set that achieves a sensitivity and specificity of 90% and 95% and may lead to the development of efficient ab-initio pre- miRNA discovery tools.
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.
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.
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.
miReader: Discovering Novel miRNAs in Species without Sequenced Genome
A novel approach, miReader, has been introduced which could discover novel miRNAs without any dependence upon genomic/reference sequences, and is expected to cause a positive impact over the area of miRNA discovery in majority of species, where genomic sequence availability would not be a compulsion any more.
Human microRNA prediction through a probabilistic co-learning model of sequence and structure
The study suggests that the miRNA gene family may be more abundant than previously anticipated, and confer highly extensive regulatory networks on eukaryotic cells.
miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades
microRNAs (miRNAs) are a large class of small non-coding RNAs which post-transcriptionally regulate the expression of a large fraction of all animal genes and are important in a wide range of
Unique folding of precursor microRNAs: quantitative evidence and implications for de novo identification.
This large-scale characterization analysis reveals that pre-miRs are significantly different from other types of ncRNAs, pseudohairpins, m RNAs, and genomic background according to the nonparametric Kruskal-Wallis ANOVA (p<0.001).
miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants
The results demonstrate miRDeep-P as an effective and easy-to-use tool for characterizing the miRNA transcriptome in plants.
HuntMi: an efficient and taxon-specific approach in pre-miRNA identification
HuntMi represents an effective and flexible tool for identification of new microRNAs in animals, plants and viruses and ROC-select strategy proves to be superior to other methods of dealing with class imbalance problem and can possibly be used in other machine learning classification tasks.