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

scientific article published on 12 April 2016

Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants is …
instance of (P31):
scholarly articleQ13442814

External links are
P8978DBLP publication IDjournals/abi/YousefSKA16
P356DOI10.1155/2016/5670851
P932PMC publication ID4844869
P698PubMed publication ID27190509
P5875ResearchGate publication ID301244460

P50authorMüşerref Duygu Saçar DemirciQ58233734
Jens AllmerQ30524421
P2093author name stringMalik Yousef
Waleed Khalifa
P2860cites workDe novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measuresQ48415584
Can MiRBase Provide Positive Data for Machine Learning for the Detection of MiRNA Hairpins?Q51210736
Gene Selection for Cancer Classification using Support Vector MachinesQ56535529
Filter Feature Selection for One-Class ClassificationQ57739370
The role of microRNAs in human diseasesQ86838685
Interactive Tree Of Life v2: online annotation and display of phylogenetic trees made easyQ24603196
miRTarBase: a database curates experimentally validated microRNA-target interactionsQ24613026
PMRD: plant microRNA databaseQ24644711
miRBase: tools for microRNA genomicsQ24649872
MaturePred: efficient identification of microRNAs within novel plant pre-miRNAsQ27313236
The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14Q27860849
Biogenesis of small RNAs in animalsQ27861013
Can MiRBase provide positive data for machine learning for the detection of MiRNA hairpins?Q30606480
MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop featuresQ33776283
MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequencesQ33877000
Computational prediction of microRNAs from Toxoplasma gondii potentially regulating the hosts' gene expressionQ35543634
Lithium protects against paraquat neurotoxicity by NRF2 activation and miR-34a inhibition in SH-SY5Y cellsQ35657214
MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined featuresQ35914053
Specialization and evolution of endogenous small RNA pathwaysQ36974244
Machine learning methods for microRNA gene predictionQ38165849
Prediction and Validation of Disease Genes Using HeteSim ScoresQ39987211
Improved and Promising Identification of Human MicroRNAs by Incorporating a High-Quality Negative Set.Q40555764
EPO Mediates Neurotrophic, Neuroprotective, Anti-Oxidant, and Anti-Apoptotic Effects via Downregulation of miR-451 and miR-885-5p in SH-SY5Y Neuron-Like CellsQ41855741
Learning from positive examples when the negative class is undetermined--microRNA gene identificationQ41963246
microPred: effective classification of pre-miRNAs for human miRNA gene prediction.Q45905788
Computational and bioinformatics methods for microRNA gene prediction.Q45958215
PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAsQ45962058
Defining and providing robust controls for microRNA predictionQ48003274
P275copyright licenseCreative Commons Attribution 4.0 InternationalQ20007257
P6216copyright statuscopyrightedQ50423863
P921main subjectmicroRNAQ310899
feature selectionQ446488
one-class classificationQ7092302
P304page(s)5670851
P577publication date2016-04-12
P1433published inAdvances in BioinformaticsQ26842273
P1476titleFeature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants
P478volume2016

Reverse relations

cites work (P2860)
Q42029847Delineating the impact of machine learning elements in pre-microRNA detection.
Q90468581MicroRNA prediction based on 3D graphical representation of RNA secondary structures
Q39639869The impact of feature selection on one and two-class classification performance for plant microRNAs.

Search more.