• Corpus ID: 235670155

Machine learning for plant microRNA prediction: A systematic review

@article{Jayasundara2021MachineLF,
  title={Machine learning for plant microRNA prediction: A systematic review},
  author={Shyaman Jayasundara and Sandali Lokuge and Puwasuru Ihalagedara and Damayanthi Herath},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.15159}
}
MicroRNAs (miRNAs) are endogenous small non-coding RNAs that play an important role in posttranscriptional gene regulation. However, the experimental determination of miRNA sequence and structure is both expensive and time-consuming. Therefore, computational and machine learning-based approaches have been adopted to predict novel microRNAs. With the involvement of data science and machine learning in biology, multiple research studies have been conducted to find microRNAs with different… 

Figures and Tables from this paper

miRNAFinder: A Comprehensive Web Resource for Plant Pre-microRNA Classification

This study presents a multilayer perceptron (MLP) based classifier implemented using 180 features under sequential, structural, and thermodynamic feature categories for plant pre-miRNA identification, and introduces a novel dataset to train and test machine learning models.

MicroRNA-mediated bioengineering for climate-resilience in crops

The main emphasis has been given to the exploration of miRNAs for development of bioengineered climate-smart crops that can withstand changing climates and stressful environments, including combination of stresses, with very less or no yield penalties.

References

SHOWING 1-10 OF 64 REFERENCES

microRPM: a microRNA prediction model based only on plant small RNA sequencing data

An effective method was developed to identify miRNAs from non-model plants based only on NGS datasets and was compiled as a user-friendly program called microRPM (miRNA Prediction Model), which is freely available at http://microRPM.itps.ncku.edu.tw.

Computational methods for the ab initio identification of novel microRNA in plants: a systematic review

A need for more stringent plant-focused miRNA identification studies to prevent further propagation of biologically questionable miRNA sequences is suggested.

miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences

A novel algorithm for predicting miRNAs named miRLocator, which is based on machine learning techniques and sequence and structural features extracted from miRNA:miRNA* duplexes, is introduced to aid researchers interested in discovering mi RNAs from model and non-model plant species.

MaturePred: Efficient Identification of MicroRNAs within Novel Plant Pre-miRNAs

The prediction method, MaturePred, can accurately predict plant miRNAs and achieve higher prediction accuracy compared with the existing methods, and a prediction model with animal data to predict animal miRN as well as confirms the efficiency of the miRNA prediction method.

PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs

The ability of PlantMiRNAPred to discern real and pseudo pre-miRNAs provides a viable method for discovering new non-homologous plant pre- miRNAs.

Prediction of plant pre-microRNAs and their microRNAs in genome-scale sequences using structure-sequence features and support vector machine

An integrated classification model, miPlantPreMat, based on structure-sequence features and SVM was developed, which achieved approximately 90% accuracy using plant datasets from nine plant species, including Arabidopsis thaliana, Glycine max, Oryza sativa, Physcomitrella patens, Medicago truncatula, Sorghum bicolor,Arabidopsis lyrata, Zea mays and Solanum lycopersicum.

Accurate Plant MicroRNA Prediction Can Be Achieved Using Sequence Motif Features

The resulting model, called MotifmiRNAPred, was tested on known pre-miRNAs listed in miRBase and its accuracy was compared to existing approaches in the field and it is believed that this approach is useful for prediction of pre- miRNAs in plants without per species adjustment.

Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees

A cross-species plant miRNA predictor is presented with 84.08% sensitivity and 98.53% specificity based on rigorous testing by leave-one-out validation.

plantMirP: an efficient computational program for the prediction of plant pre-miRNA by incorporating knowledge-based energy features.

A promising SVM-based program, plantMirP, is developed for predicting plant pre-miRNAs by incorporating knowledge-based energy features that has very high discriminatory power and is shown to be a valuable tool for miRNA-related studies.

MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences

A software tool, miRPara, that predicts most probable mature miRNA coding regions from genome scale sequences in a species specific manner and achieves an accuracy of up to 80% against experimentally verified mature miRNAs, making it one of the most accurate methods available.
...