A comparison study between one-class and two-class machine learning for MicroRNA target detection

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Journal of Biomedical Science and Engineering

ISSN Print: 1937-6871
ISSN Online: 1937-688X
www.scirp.org/journal/jbise
E-mail: jbise@scirp.org

Google-based Impact Factor: 1.68
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"A comparison study between one-class and two-class machine learning for MicroRNA target detection"
written by Malik Yousef, Naim Najami, Waleed Khalifav,
published by Journal of Biomedical Science and Engineering, Vol.3 No.3, 2010

has been cited by the following article(s):

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[1]	Computational detection of microRNA targets
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[2]	miRNAFinder: A comprehensive web resource for plant Pre-microRNA classification
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[5]	Deep Learning for the discovery of new pre-miRNAs: Helping the fight against COVID-19
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[6]	Machine learning for plant microRNA prediction: A systematic review
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[7]	MultiKOC: Multi-One-Class Classifier Based K-Means Clustering
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[8]	One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios
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[9]	miRNAFinder: A pre-microRNA classifier for plants and analysis of feature impact
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[10]	BIOINFORMATICS GENOME ANALYSIS
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[23]	A systematic study of the class imbalance problem in convolutional neural networks
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[24]	High class-imbalance in pre-miRNA prediction: a novel approach based on deepSOM
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[25]	Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants
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[26]	Feature Selection for MicroRNA Target Prediction
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[27]	Feature selection for microRNA target prediction comparison of one-class feature selection methodologies
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[28]	Feature selection for microRNA target prediction-comparison of one-class feature selection methodologies
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[29]	svclassify: a method to establish benchmark structural variant calls
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[30]	Sequence Motif-Based One-Class Classifiers Can Achieve Comparable Accuracy to Two-Class Learners for Plant microRNA Detection
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[33]	Analysis of microRNA precursors in multiple species by data mining techniques
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[1]	miRNomics Methods in Molecular Biology, 2022 DOI:10.1007/978-1-0716-1170-8_10

[2]	miRNAFinder: A comprehensive web resource for plant Pre-microRNA classification Biosystems, 2022 DOI:10.1016/j.biosystems.2022.104662

[3]	miRNAFinder: A comprehensive web resource for plant Pre-microRNA classification Biosystems, 2022 DOI:10.1016/j.biosystems.2022.104662

[4]	One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios ICT Express, 2021 DOI:10.1016/j.icte.2021.03.001

[5]	MultiKOC: Multi-One-Class Classifier Based K-Means Clustering Algorithms, 2021 DOI:10.3390/a14050134

[6]	Novel SARS-CoV-2 encoded small RNAs in the passage to humans Bioinformatics, 2021 DOI:10.1093/bioinformatics/btaa1002

[7]	One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios ICT Express, 2021 DOI:10.1016/j.icte.2021.03.001

[8]	One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios ICT Express, 2021 DOI:10.1016/j.icte.2021.03.001

[9]	Deep Learning for the discovery of new pre-miRNAs: Helping the fight against COVID-19 Machine Learning with Applications, 2021 DOI:10.1016/j.mlwa.2021.100150

[10]	Genome-wide discovery of pre-miRNAs: comparison of recent approaches based on machine learning Briefings in Bioinformatics, 2020 DOI:10.1093/bib/bbaa184

[11]	miRNAFinder: A pre-microRNA classifier for plants and analysis of feature impact 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2020 DOI:10.1109/CIBCB48159.2020.9277723

[12]	Novel SARS-CoV-2 encoded small RNAs in the passage to humans Bioinformatics, 2020 DOI:10.1093/bioinformatics/btaa1002

[13]	Database and Expert Systems Applications Communications in Computer and Information Science, 2019 DOI:10.1007/978-3-030-27684-3_7

[14]	Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose Sensors, 2019 DOI:10.3390/s19030685

[15]	A Linked Data Browser with Recommendations 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), 2018 DOI:10.1109/ICTAI.2018.00038

[16]	High Class-Imbalance in pre-miRNA Prediction: A Novel Approach Based on deepSOM IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017 DOI:10.1109/TCBB.2016.2576459

[17]	svclassify: a method to establish benchmark structural variant calls BMC Genomics, 2016 DOI:10.1186/s12864-016-2366-2

[18]	Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants Advances in Bioinformatics, 2016 DOI:10.1155/2016/5670851

[19]	Sequence Motif-Based One-Class Classifiers Can Achieve Comparable Accuracy to Two-Class Learners for Plant microRNA Detection Journal of Biomedical Science and Engineering, 2015 DOI:10.4236/jbise.2015.810065

[20]	Computational Approaches for Biomarker Discovery Journal of Intelligent Learning Systems and Applications, 2014 DOI:10.4236/jilsa.2014.64012

[21]	One class random forests Pattern Recognition, 2013 DOI:10.1016/j.patcog.2013.05.022

[22]	Information Technology Convergence Lecture Notes in Electrical Engineering, 2013 DOI:10.1007/978-94-007-6996-0_56

[23]	Information Technology Convergence Lecture Notes in Electrical Engineering, 2013 DOI:10.1007/978-94-007-6996-0_56

Follow SCIRP

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