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Feature Selection for MicroRNA Target Prediction - Comparison of One-Class Feature Selection Methodologies
2016
Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies
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In this study, we present a feature selection approach for applying one-class classification to the prediction of miRNA targets. ...
Artificially generating the negative class data can be based on unreliable assumptions. Several studies have applied two-class machine learning to predict microRNAs (miRNAs) and their target. ...
ACKNOWLEDGEMENTS The work was supported by the Scientific and Technological Research Council of Turkey [grant number 113E326] to JA. ...
doi:10.5220/0005701602160225
dblp:conf/biostec/YousefAK16
fatcat:htjihucumrbdvcjiqy737kyp4a
Accurate Plant MicroRNA Prediction Can Be Achieved Using Sequence Motif Features
2016
Journal of Intelligent Learning Systems and Applications
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We here select a subset of the previously described features and add sequence motifs as new features. ...
For the prediction of pre-miRNAs, usually machine learning approaches are employed. ...
Acknowledgements The work was supported by the Ministry of Science, Israel to MY and WK. and the Scientific and Technological Research Council of Turkey [113E326 to JA]. ...
doi:10.4236/jilsa.2016.81002
fatcat:45bueolypjdfvjkaadvkrlwure
Naïve Bayes for microRNA target predictions—machine learning for microRNA targets
2007
Computer applications in the biosciences : CABIOS
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Results: The application of machine learning techniques to the features we have used is a useful and general approach for microRNA target gene prediction. ...
Motivation: Most computational methodologies for miRNA:mRNA target gene prediction use the seed segment of the miRNA and require cross-species sequence conservation in this region of the mRNA target. ...
conducted a survey and a comparison of the 5 most used tools for mammalian target prediction and indicated that 30% of the experimentally validated target sites are nonconserved, supporting the need for ...
doi:10.1093/bioinformatics/btm484
pmid:17925304
fatcat:cp6pn336yzbljh4jadvpn4nrqa
Flanking region sequence information to refine microRNA target predictions
2010
Journal of Biosciences
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, suggesting better performance by our methodology and a possible role of fl anking regions in microRNA targeting control. ...
Several microRNA:target prediction softwares have been developed based upon various assumptions and the majority of them consider the free energy of binding of a target to its microRNA and seed conservation ...
Acknowledgements We thank Mr Amit Chaurasia and Dr Mitali Mukerji of Institute of Genomics and Integrative Biology, Delhi, for sharing TFBS data on human sequences using the TP database. ...
doi:10.1007/s12038-010-0013-7
pmid:20413915
fatcat:dklnccihenduflpzn5uljfcbvm
Sequence Motif-Based One-Class Classifiers Can Achieve Comparable Accuracy to Two-Class Learners for Plant microRNA Detection
2015
Journal of Biomedical Science and Engineering
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In this study it was our aim to analyze different methods applying one-class approaches and the effectiveness of motifbased features for prediction of plant miRNA genes. ...
One-class classifiers, on the other hand, use only the information for the target class (miRNA). ...
Acknowledgements The work was supported by the Scientific and Technological Research Council of Turkey [grant number 113E326] to JA. Conflict of interest: none declared. ...
doi:10.4236/jbise.2015.810065
fatcat:37yizm54tjglleovpgtlqzq27q
The impact of feature selection on one and two-class classification performance for plant microRNAs
2016
PeerJ
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the feature selection methodologies. ...
We conclude that feature selection is crucially important for OCC and that it can perform on parwith TCC given the proper set of features. ...
For OCC feature selection nothing has been done in the area of pre-miRNA detection while one study investigated feature selection based on OCC for mature miRNA prediction (Xuan et al., 2011a) . ...
doi:10.7717/peerj.2135
pmid:27366641
pmcid:PMC4924126
fatcat:rjhxtcfzyvaknbltkxb26piglm
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 ...
Acknowledgments The work was supported by the Scientific and Technological Research Council of Turkey (Grant no. 113E326) to Jens Allmer. ...
doi:10.1155/2016/5670851
pmid:27190509
pmcid:PMC4844869
fatcat:ckax6gbgijeu3bbn6c5hr6yyeq
RFMirTarget: Predicting Human MicroRNA Target Genes with a Random Forest Classifier
2013
PLoS ONE
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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 ...
Indeed, the best results reported so far in terms of specificity and sensitivity are associated to machine learning-based methods for microRNA-target prediction. ...
MiRanda is an algorithm for the detection of potential microRNA target sites in genomic sequences. ...
doi:10.1371/journal.pone.0070153
pmid:23922946
pmcid:PMC3724815
fatcat:6phsiwri3revfg76dievk5djby
ncRNA-Class Web Tool: Non-coding RNA Feature Extraction and Pre-miRNA Classification Web Tool
[chapter]
2012
IFIP Advances in Information and Communication Technology
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efficient environment for the effective calculation of a set of 58 sequential, thermodynamical and structural features of non-coding RNAs, plus a tool for the accurate prediction of miRNAs. ...
Many independent tools have already been developed for the efficient calculation of such features but to the best of our knowledge there does not exist any integrative approach for this task. ...
This research has been co-financed by the European Union (European Social Fund -ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic ...
doi:10.1007/978-3-642-33412-2_65
fatcat:wrzgzcsx4bfbnnwbttzmtrib7a
Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites
2010
Genome Biology
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The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. ...
mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. ...
://www.microRNA.org target prediction resource; and Markus Hafner and Tom Tuschl for providing the PAR-CLIP data. ...
doi:10.1186/gb-2010-11-8-r90
pmid:20799968
pmcid:PMC2945792
fatcat:zr2nhrs4fffzzpg5teo42pbxe4
miREE: miRNA recognition elements ensemble
2011
BMC Bioinformatics
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Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. ...
Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. ...
Authors' contributions AA, EF, EM participated in the design of the study and coordination. PR participated in the design of the study and she developed the algorithm. ...
doi:10.1186/1471-2105-12-454
pmid:22115078
pmcid:PMC3265527
fatcat:kqf2323jzjgztcey2axhh4jr3e
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 ...
To date, several computational methodologies have been proposed to precisely identify the class as well as sub-cellular localization patterns of RNAs). ...
A performance comparison with baseline RNN and state-of-the-art predictive methodologies showed that the proposed CNN model achieved the top accuracy of 96% on the benchmark dataset, outperforming previous ...
doi:10.3390/ijms22168719
pmid:34445436
pmcid:PMC8395733
fatcat:l66viluvf5aqvimsyvmowvpjuy
Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data
2020
Entropy
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the discovery of new potential targets for treatment. ...
In biological data analysis, most of the computational feature selection methodologies were taken from other fields, without considering the nature of the biological data. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/e23010002
pmid:33374969
fatcat:bziw2njwlzdq3nhsf3hzeo7rh4
Specificity Enhancement in microRNA Target Prediction through Knowledge Discovery
[chapter]
2010
Machine Learning
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Erno Vreugdenhil for discussing some biological implications of the results and Peter van de Putten for suggestions on the use of WEKA. ...
This research has been partially supported by the BioRange program of the Netherlands BioInformatics Centre (BSIK grant). ...
Additional strategies for target prediction are necessary and we elaborate on one particular group of microRNAs; i.e. those that might bind to the same target. ...
doi:10.5772/9140
fatcat:gedflwuorjdvtgrcmhgs2534lq
STarMir Tools for Prediction of microRNA Binding Sites
[chapter]
2016
Msphere
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This chapter provides protocols for using the STarMir web server for improved predictions of miRNA binding sites on a target mRNA. ...
MicroRNAs (miRNAs) are a class of endogenous short non-coding RNAs that regulate gene expression by targeting messenger RNAs (mRNAs), which results in translational repression and/or mRNA degradation. ...
.), National Institutes of Health (GM099811 to Y.D. and J. L.). ...
doi:10.1007/978-1-4939-6433-8_6
pmid:27665594
pmcid:PMC5353976
fatcat:hslplso2bjhfnbl2fxalbe2n6i
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