Artikel
Open Access
A Machine Learning Approach for MicroRNA Precursor Prediction in Retro-transcribing Virus Genomes
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Müşerref Duygu Saçar Demirci
Veröffentlicht/Copyright:
20. April 2017
Published Online: 2017-4-20
Published in Print: 2016-12-1
© 2016 The Author(s). Published by Journal of Integrative Bioinformatics.
Artikel in diesem Heft
- Computational miRNomics
- A Machine Learning Approach for MicroRNA Precursor Prediction in Retro-transcribing Virus Genomes
- Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data
- Dynamics of miRNA biogenesis and nuclear transport
- Current Progress of High-Throughput MicroRNA Differential Expression Analysis and Random Forest Gene Selection for Model and Non-Model Systems: an R Implementation
- miRquant 2.0: an Expanded Tool for Accurate Annotation and Quantification of MicroRNAs and their isomiRs from Small RNA-Sequencing Data
- Differential Expression of Hyperhydricity Responsive Peach MicroRNAs
Creative Commons
BY-NC-ND 4.0
Artikel in diesem Heft
- Computational miRNomics
- A Machine Learning Approach for MicroRNA Precursor Prediction in Retro-transcribing Virus Genomes
- Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data
- Dynamics of miRNA biogenesis and nuclear transport
- Current Progress of High-Throughput MicroRNA Differential Expression Analysis and Random Forest Gene Selection for Model and Non-Model Systems: an R Implementation
- miRquant 2.0: an Expanded Tool for Accurate Annotation and Quantification of MicroRNAs and their isomiRs from Small RNA-Sequencing Data
- Differential Expression of Hyperhydricity Responsive Peach MicroRNAs
