PlantMiRNAPred specifications


Unique identifier OMICS_08006
Name PlantMiRNAPred
Interface Web user interface
Restrictions to use None
Input data Sequence
Input format FASTA
Computer skills Basic
Stability Stable
Maintained No


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Publication for PlantMiRNAPred

PlantMiRNAPred in publications

PMCID: 4867574
PMID: 27181057
DOI: 10.1038/srep25941

[…] which extract from protein coding regions and have similar stem-loop structures with genuine pre-mirnas but have not been reported as pre-mirnas, such as triplet, mipred, heteromirpred, micropred, plantmirnapred ,mirnadetect and the algorithm developed huang et al.. recently, a series of up-to-data methods are proposed for pre-mirna identification with a detailed research on feature selection […]

PMCID: 4844869
PMID: 27190509
DOI: 10.1155/2016/5670851

[…] of 98.80% accuracy for zea mays, feature selection using clustering methods was able to increase the accuracy achieved in our previous studies by 1–4% and the two-class classification benchmark plantmirnapred by ~0.5%. this study showed that feature selection is effective despite varying data quality. concluding, it seems important to devise effective data selection methodologies to ensure […]

PMCID: 4924126
PMID: 27366641
DOI: 10.7717/peerj.2135

[…] patens (ppt), arabidopsis thaliana (ath), populus trichocarpa (ptc), and oryza sativa (osa) make up the positive dataset. negative examples for mirnas consisted of 980 pseudo pre-mirnas from the plantmirnapred dataset (, ). for these data, all pre-mirna features were calculated as described previously (, ; , ; , ). we chose plant pre-mirnas with large amount of pre-mirna examples […]

PMCID: 4657081
PMID: 26640803
DOI: 10.1155/2015/960108

[…] classification methods have taken the imbalance problem into account. triple-svm [] adopted random undersampling method to select the same number of negative samples with that of positive samples. plantmirnapred [] clustered the positive and negative samples according to their distribution. the equal number of representative positive and negative samples was selected as the training data. […]

PMCID: 3861888
PMID: 24396270
DOI: 10.2174/13892029113149990005

[…] randomization test, uses a novel machine-learning technique based on random forest algorithm to identify putative mirna precursors and seems to provide high sensitivity and specificity. furthermore, plantmirnapred [] can classify plant mirna precursors efficiently by svm together with feature and sample selection strategies. it selects a variety of features from both primary sequence […]

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PlantMiRNAPred institution(s)
Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China
PlantMiRNAPred funding source(s)
Natural Science Foundation of China (60932008 and 60871092); Fundamental Research Funds for the Central Universities (HIT.ICRST.2010 022); Returned Scholar Foundation of Educational Department of Heilongjiang Province (1154hz26); Natural Science Foundation (Grant CCF-0546345 to Y.H.).

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