Now showing items 1-6 of 6
Categorization of species based on their microRNAs employing sequence motifs, information-theoretic sequence feature extraction, and k-mers
Background: 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 are used throughout all kingdoms and ...
On the performance of pre-microRNA detection algorithms
(Nature Publishing Group, 2017-12)
MicroRNAs are crucial for post-transcriptional gene regulation, and their dysregulation has been associated with diseases like cancer and, therefore, their analysis has become popular. The experimental discovery of miRNAs ...
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 ...
Machine learning methods for microRNA gene prediction
(Humana Press, 2014)
MicroRNAs (miRNAs) are single-stranded, small, noncoding RNAs of about 22 nucleotides in length, which control gene expression at the posttranscriptional level through translational inhibition, degradation, adenylation, ...
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 ...