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Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants
2016
Advances in Bioinformatics
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In this work, we employ feature selection procedures in conjunction with one-class classification and show that there is up to 36% difference in accuracy among these feature selection methods. ...
The best feature set allowed the training of a one-class classifier which achieved an average accuracy of ~95.6% thereby outperforming previous two-class-based plant miRNA detection approaches by about ...
In order to test the impact of feature selection on the classification accuracy, four negative and four positive feature selection methods were designed. ...
doi:10.1155/2016/5670851
pmid:27190509
pmcid:PMC4844869
fatcat:ckax6gbgijeu3bbn6c5hr6yyeq
The impact of feature selection on one and two-class classification performance for plant microRNAs
2016
PeerJ
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Although, in general, two-class classification (TCC) is used in the field; because negative examples are hard to come by, one-class classification (OCC) has been tried for pre-miRNA detection. ...
Feature selection was very successful for OCC where the best feature selection method achieved an average accuracy of 95.6%, thereby being ∼29% better than the worst method which achieved 66.9% accuracy ...
These competing methods using different strategies for FS in pre-miRNA detection do not refer to OCC. However, they clearly show that feature selection has a large impact on model performance. ...
doi:10.7717/peerj.2135
pmid:27366641
pmcid:PMC4924126
fatcat:rjhxtcfzyvaknbltkxb26piglm
Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data
2016
Journal of Integrative Bioinformatics
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The comparison was applied to seven different plant microRNA species considering eight feature selection methods. ...
In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. ...
To investigate the impact of feature selection on model performance for OCC and TCC, four negative and four positive feature selection methods were designed. ...
doi:10.1515/jib-2016-304
fatcat:au3kzxssn5hhpojjbrnzsqabrm
Accurate Plant MicroRNA Prediction Can Be Achieved Using Sequence Motif Features
2016
Journal of Intelligent Learning Systems and Applications
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A large body of research is available for computational pre-miRNA detection in animals, but less within the plant kingdom. ...
We here select a subset of the previously described features and add sequence motifs as new features. ...
To see the impact of motifs on the classification accuracy, two models were trained for all datasets, one which uses both motifs and n-grams and one which only relies only on the latter. ...
doi:10.4236/jilsa.2016.81002
fatcat:45bueolypjdfvjkaadvkrlwure
SUPPORT VECTOR MACHINE FOR CLASSIFICATION OF HIV, PLANT AND ANIMAL miRNA'S
English
2011
International Journal of Bioinformatics Research
English
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The new SVM learning algorithm called Weka LibSVM has been used for classification of plant and animal and HIVmiRNA. The model has been tested on available data and it gives results with 95% accuracy. ...
MicroRNAs (miRNA's) constitute a large family of non coding RNAs that function to regulate gene expression. ...
., Bhopal, India for providing support in the form of Bioinformatics infrastructure facility to carry out the research work. ...
doi:10.9735/0975-3087.3.2.202-206
fatcat:3h7oa3qxu5csbhhyksvczded6i
Machine learning for plant microRNA prediction: A systematic review
[article]
2021
arXiv
pre-print
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This systematic review focuses on the machine learning methods developed for miRNA identification in plants. ...
Our findings highlight the need for plant-specific computational methods for miRNA identification. ...
Buwani Manuweera, PhD Student, Montana State University, USA for providing assistance and guidance in preparing the manuscript. ...
arXiv:2106.15159v1
fatcat:5ykoisyhi5dbditcv7am44eyqi
MicroRNA identification using linear dimensionality reduction with explicit feature mapping
2013
BMC Proceedings
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microRNAs are a class of small RNAs, about 20 nt long, which regulate cellular processes in animals and plants. Identifying microRNAs is one of the most important tasks in gene regulation studies. ...
Also, explicitly mapping data onto a high dimensional space could be a useful alternative to kernel-based methods for large datasets with a small number of features. ...
Declarations The publication costs for this article were funded by the Natural Sciences and Engineering Council of Canada, NSERC. ...
doi:10.1186/1753-6561-7-s7-s8
pmid:24564997
pmcid:PMC4044883
fatcat:aswcvuzizfdlrcsxwplalhd7tm
Naïve Bayes Classifier for Classification of Plantand Animal miRNA
2010
Journal of clean energy technologies
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In view of above a machine learning models has been developed for classification of plant and animal miRNA using Naive Bayes classifier. ...
The model has been tested on available data and it gives results with 85.71% accuracy. ...
., India for Bioinformatics infrastructure facility. ...
doi:10.7763/ijcte.2010.v2.179
fatcat:upknvbqjindt7a4rk55reocnhu
Categorization of species based on their microRNAs employing sequence motifs, information-theoretic sequence feature extraction, and k-mers
2017
EURASIP Journal on Advances in Signal Processing
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However, for obtaining a high performance, a sufficiently large phylogenetic distance between the species and sufficiently high number of pre-miRNAs in the training set is required. ...
Diseases like cancer can manifest themselves through changes in protein abundance, and microRNAs (miRNAs) play a key role in the modulation of protein quantity. ...
MicroRNAs have been shown to exist in a variety of species ranging from viruses [2] to plants [3] . ...
doi:10.1186/s13634-017-0506-8
fatcat:eg4uvb4nvja5jdhsgmqxzqj5du
TargetSpy: a supervised machine learning approach for microRNA target prediction
2010
BMC Bioinformatics
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On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. ...
It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. ...
Acknowledgements We are grateful to Hans-Werner Mewes, Florian Büttner and Thorsten Schmidt for careful reading of the manuscript and many useful comments. ...
doi:10.1186/1471-2105-11-292
pmid:20509939
pmcid:PMC2889937
fatcat:otb4nrcqbzdmvgroodwglwq3ly
Computational Characterization of Exogenous MicroRNAs that Can Be Transferred into Human Circulation
2015
PLoS ONE
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Through in-depth bioinformatics analysis, 8 groups of discriminative features have been used to characterize human circulating microRNAs and infer the likelihood that a microRNA will get transferred into ...
Specifically, we analyzed all publicly available microRNAs, a total of 34,612 from 194 species, with 1,102 features derived from the micro-RNA sequence and structure. ...
Scott Baier for his assistance in preparing RNA samples for NGS analysis. The Holland Computing Center at UNL has provided us the computational facilities for data analysis. ...
doi:10.1371/journal.pone.0140587
pmid:26528912
pmcid:PMC4631372
fatcat:w7ovzvy4wreo7nz54k55isvk6q
RFMirTarget: Predicting Human MicroRNA Target Genes with a Random Forest Classifier
2013
PLoS ONE
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Following this trend, in the current paper we discuss and explore a microRNA-target prediction method based on a random forest classifier, namely RFMirTarget. ...
Finally, tests performed with RFMirTarget show the benefits of feature selection even for a classifier with embedded feature importance analysis, and the consistency between relevant features identified ...
Building a RF Model Based on the Top Ranked Features Based on the features ranking of Table 4 , we perform a restricted forward feature selection: we assess features impact to the model's predictive accuracy ...
doi:10.1371/journal.pone.0070153
pmid:23922946
pmcid:PMC3724815
fatcat:6phsiwri3revfg76dievk5djby
Heterogeneous ensemble approach with discriminative features and modified-SMOTEbagging for pre-miRNA classification
2012
Nucleic Acids Research
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An ensemble classifier approach for microRNA precursor (pre-miRNA) classification was proposed based upon combining a set of heterogeneous algorithms including support vector machine (SVM), k-nearest neighbors ...
The ensemble model was applied to animal, plant and virus pre-miRNA and achieved high accuracy, >93%. ...
in editing and proofreading the manuscript. ...
doi:10.1093/nar/gks878
pmid:23012261
pmcid:PMC3592496
fatcat:ocdrrri7wvbpzioht2wjbk6osy
Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs
2021
International Journal of Molecular Sciences
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We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence ...
It identifies research gaps, heterogeneity, and challenges in the development of computational approaches for RNA sequence analysis. ...
In addition, this database provides a large amount of annotated sequences for various classes of ncRNAs. ...
doi:10.3390/ijms22168719
pmid:34445436
pmcid:PMC8395733
fatcat:l66viluvf5aqvimsyvmowvpjuy
Supervised and Unsupervised Classification of lncRNA Subtypes
[article]
2020
bioRxiv
pre-print
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gene distal from the snoRNAs or miRNA payload is used for classification. ...
In contrast to their highly conserved and heavily structured payload, the host genes feature poorly conserved sequences. ...
Acknowledgments: We thank Stephanie Kehr for insightful discussions and her advice on all things snoRNA. ...
doi:10.1101/2020.07.20.211433
fatcat:ytwbujypvfdipeukgpjusgcmtq
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