PlantMiRNAPred specifications

Information


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

Maintainer


This tool is not available anymore.

Publication for PlantMiRNAPred

PlantMiRNAPred in publications

 (6)
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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