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Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants

Malik Yousef, Müşerref Duygu Saçar Demirci, Waleed Khalifa, Jens Allmer
2016 Advances in Bioinformatics  
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

Waleed Khalifa, Malik Yousef, Müşerref Duygu Saçar Demirci, Jens Allmer
2016 PeerJ  
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

Malik Yousef, Waleed Khalifa, Loai AbdAllah
2016 Journal of Integrative Bioinformatics  
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

Malik Yousef, Jens Allmer, Waleed Khalifa
2016 Journal of Intelligent Learning Systems and Applications  
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


2011 International Journal of Bioinformatics Research  
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]

Shyaman Jayasundara, Sandali Lokuge, Puwasuru Ihalagedara, Damayanthi Herath
2021 arXiv   pre-print
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

Navid Shakiba, Luis Rueda
2013 BMC Proceedings  
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

Bhasker Pant, Kumud Pant, K. R. Pardasani
2010 Journal of clean energy technologies  
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

Malik Yousef, Dawit Nigatu, Dalit Levy, Jens Allmer, Werner Henkel
2017 EURASIP Journal on Advances in Signal Processing  
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

Martin Sturm, Michael Hackenberg, David Langenberger, Dmitrij Frishman
2010 BMC Bioinformatics  
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

Jiang Shu, Kevin Chiang, Janos Zempleni, Juan Cui, Ying Xu
2015 PLoS ONE  
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

Mariana R. Mendoza, Guilherme C. da Fonseca, Guilherme Loss-Morais, Ronnie Alves, Rogerio Margis, Ana L. C. Bazzan, Mikael Boden
2013 PLoS ONE  
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

Supatcha Lertampaiporn, Chinae Thammarongtham, Chakarida Nukoolkit, Boonserm Kaewkamnerdpong, Marasri Ruengjitchatchawalya
2012 Nucleic Acids Research  
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

Muhammad Nabeel Asim, Muhammad Ali Ibrahim, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed
2021 International Journal of Molecular Sciences  
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]

Rituparno Sen, Joerg Fallmann, Maria Emília M. T. Walter, Peter F Stadler
2020 bioRxiv   pre-print
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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