ArticleBioinformation2016
A method for clustering of miRNA sequences using fragmented programming.
Article in Bioinformation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- ComparativeFrontiers in plant science · 2026Article
- In Silico Analysis of miRNA-mRNA Binding Sites inPlants (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clustering of miRNA sequences is an important problem in molecular genetics associated cellular biology. Thousands of such sequences are known today through advancement in sophisticated molecular tools, sequencing techniques, computational resources and rule based mathematical models. Analysis of such large-scale miRNA sequences for inferring patterns towards deducing cellular function is a great challenge in modern molecular biology. Therefore, it is of interest to develop mathematical models specific for miRNA sequences. The process is to group (cluster) such miRNA sequences using well-defined known features. We describe a method for clustering of miRNA sequences using fragmented programming. Subsequently, we illustrated the utility of the model using a dendrogram (a tree diagram) for publically known A.thaliana miRNA nucleotide sequences towards the inference of observed conserved patterns.
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Registered trials
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