PlantMirP-Rice: An Efficient Program for Rice Pre-miRNA Prediction

Zhang, Huiyu; Wang, Hua; Yao, Yuangen; Yi, Ming

doi:10.3390/genes11060662

Cited by 5 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this comparative benchmarking, the performances of eight compared software were studied across the Datasets “A”, “B”, and “C”. The compared tools covered the classical machine learning as well as recently developed Deep Learning approaches for pre-miRNA discovery: miPlantPreMat (SVM based), HuntMi (Ensemble method of Random Forest), PlantmirP-Rice (Ensembl method of Random Forest) (57), microPred (SVM based), plantMiRP (SVM based), mirDNN (convolutional deep residual networks), deepMir (CNN based) and deepSOM (deep learning based SOM). Besides this, the benchmarking has also considered three different datasets to carry out a fully unbiased assessment of performance of these tools across different datasets.…”

Section: Resultsmentioning

confidence: 99%

Sentences, Words, Attention: A “Transforming” Aphorism for the Discovery of pre-miRNA Regions across Plant Genomes

Gupta

Saini

Kumar

et al. 2022

Preprint

View full text Add to dashboard Cite

Discovering pre-miRNAs is the core of miRNA discovery. Using traditional sequence/structural features many tools have been published to discover miRNAs. However, in practical applications like genomic annotations, their actual performance has been far away from acceptable. This becomes more grave in plants where unlike animals pre-miRNAs are much more complex and difficult to identify. This is reflected by the huge gap between the available software for miRNA discovery and species specific miRNAs information for animals and plants. Here, we present miWords, an attention based genomic language processing transformer and context scoring deep-learning approach, with an optional sRNA-seq guided CNN module to accurately identify pre-miRNA regions in plant genomes. During a comprehensive bench-marking the transformer part of miWords alone significantly outperformed the compared published tools with consistent performance while breaching accuracy of 98% across a large number of experimentally validated data. Performance of miWords was also evaluated across Arabidopsis genome where also miWords, even without using its sRNA-seq reads module, outperformed those software which essentially require sRNA-seq reads to identify miRNAs. miWords was run across the Tea genome, reporting 803 pre-miRNA regions, all validated by sRNA-seq reads from multiple samples, and 10 randomly selected cases re-validated by qRT-PCR.

show abstract

Section: Resultsmentioning

confidence: 99%

Sentences, Words, Attention: A “Transforming” Aphorism for the Discovery of pre-miRNA Regions across Plant Genomes

Gupta

Saini

Kumar

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…SVM was used to detect animals and plant miRNAs and pre-miRNAs detection methods [ 53 , 54 ]. Rice pre-miRNAs detection was done using random forest, achieving prediction accuracy of 93.48% [ 55 ].…”

Section: Related Workmentioning

confidence: 99%

Robust and efficient COVID-19 detection techniques: A machine learning approach

et al. 2022

View full text Add to dashboard Cite

The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandemic almost halted the global economy and is responsible for 6 million deaths with infection rates of over 524 million. With significant reservations, initially, the SARS-CoV-2 virus was suspected to be infected by and closely related to Bats. However, over the periods of learning and critical development of experimental evidence, it is found to have some similarities with several gene clusters and virus proteins identified in animal-human transmission. Despite this substantial evidence and learnings, there is limited exploration regarding the SARS-CoV-2 genome to putative microRNAs (miRNAs) in the virus life cycle. In this context, this paper presents a detection method of SARS-CoV-2 precursor-miRNAs (pre-miRNAs) that helps to identify a quick detection of specific ribonucleic acid (RNAs). The approach employs an artificial neural network and proposes a model that estimated accuracy of 98.24%. The sampling technique includes a random selection of highly unbalanced datasets for reducing class imbalance following the application of matriculation artificial neural network that includes accuracy curve, loss curve, and confusion matrix. The classical approach to machine learning is then compared with the model and its performance. The proposed approach would be beneficial in identifying the target regions of RNA and better recognising of SARS-CoV-2 genome sequence to design oligonucleotide-based drugs against the genetic structure of the virus.

show abstract

“…The recognition characteristic of k-mer and the advantages of distance-specific pair potential distinguishing between natural and non-natural structures were well combined. Furthermore, knowledge-based energy features have been firmly demonstrated to have very high discriminatory power [21,29,30]. Most recently, knowledge-based energy features have been further optimized and developed to consider position-specific information.…”

Section: Datasets and Feature Setmentioning

confidence: 99%

“…To do this, we developed plantMirP into plantMirP2. Firstly, we incorporated and optimized knowledge-based energy features, which are firstly proposed in plantMirP and further developed in our recent studies (i.e., riceMirP [29] and milRNApredictor [30]). Secondly, the parameters of the SVM model and the algorithm are optimized, and the independent dataset is updated according to the latest version of the miRBase database.…”

Section: Introductionmentioning

confidence: 99%

PlantMirP2: An Accurate, Fast and Easy-To-Use Program for Plant Pre-miRNA and miRNA Prediction

Fan

Yao

2021

Genes

Self Cite

View full text Add to dashboard Cite

MicroRNAs (miRNAs) are a kind of short non-coding ribonucleic acid molecules that can regulate gene expression. The computational identification of plant miRNAs is of great significance to understanding biological functions. In our previous studies, we have put firstly forward and further developed a set of knowledge-based energy features to construct two plant pre-miRNA prediction tools (plantMirP and riceMirP). However, these two tools cannot be used for miRNA prediction from NGS (Next-Generation Sequencing) data. In addition, for further improving the prediction performance and accessibility, plantMirP2 has been developed. Based on the latest dataset, plantMirP2 achieves a promising performance: 0.9968 (Area Under Curve, AUC), 0.9754 (accuracy), 0.9675 (sensitivity) and 0.9876 (specificity). Additionally, the comparisons with other plant pre-miRNA tools show that plantMirP2 performs better. Finally, the webserver and stand-alone version of plantMirP2 are available.

show abstract

PlantMirP-Rice: An Efficient Program for Rice Pre-miRNA Prediction

Cited by 5 publications

References 33 publications

Sentences, Words, Attention: A “Transforming” Aphorism for the Discovery of pre-miRNA Regions across Plant Genomes

Sentences, Words, Attention: A “Transforming” Aphorism for the Discovery of pre-miRNA Regions across Plant Genomes

Robust and efficient COVID-19 detection techniques: A machine learning approach

PlantMirP2: An Accurate, Fast and Easy-To-Use Program for Plant Pre-miRNA and miRNA Prediction

Contact Info

Product

Resources

About