Now showing items 1-5 of 5
MicroRNA categorization using sequence motifs and k-mers
(BioMed Central, 2017-03)
Background: Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and ...
Feature selection for microRNA target prediction comparison of one-class feature selection methodologies
Traditionally, machine learning algorithms build classification models from positive and negative examples. Recently, one-class classification (OCC) receives increasing attention in machine learning for problems where the ...
The impact of feature selection on one and two-class classification performance for plant microRNAs
(PeerJ Inc., 2016)
MicroRNAs (miRNAs) are short nucleotide sequences that form a typical hairpin structure which is recognized by a complex enzyme machinery. It ultimately leads to the incorporation of 18-24 nt long mature miRNAs into RISC ...
Feature selection has a large impact on one-class classification accuracy for micrornas in plants
(Hindawi Publishing Corporation, 2016)
MicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently ...
Distinguishing between microRNA targets from diverse species using sequence motifs and K-mers
A disease phenotype is often due to dysregulation of gene expression. Post-translational regulation of protein abundance by microRNAs (miRNAs) is, therefore, of high importance in, for example, cancer studies. MicroRNAs ...