English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Functional or non-coding RNAs are attracting more attention as they are nowpotentially considered valuable resources in the development of new drugsintended to cure several human diseases. The identification of drugs targetingthe regulatory circuits of functional RNAs depends on knowing its family, atask which is known as RNA sequence classification. State-of-the-art smallnoncoding RNA classification methodologies take secondary structural featuresas input. However, in such classification, feature extraction approaches onlytake global characteristics into account and completely oversight co-relativeeffect of local structures. Furthermore secondary structure based approachesincorporate high dimensional feature space which proves computationallyexpensive. This paper proposes a novel Robust and Precise ConvNet (RPC-snRC)methodology which classifies small non-coding RNAs sequences into theirrelevant families by utilizing the primary sequence of RNAs. RPC-snRCmethodology learns hierarchical representation of features by utilizingpositioning and occurrences information of nucleotides. To avoid exploding andvanishing gradient problems, we use an approach similar to DenseNet in whichgradient can flow straight from subsequent layers to previous layers. In orderto assess the effectiveness of deeper architectures for small non-coding RNAclassification, we also adapted two ResNet architectures having differentnumber of layers. Experimental results on a benchmark small non-coding RNAdataset show that our proposed methodology does not only outperform existingsmall non-coding RNA classification approaches with a significant performancemargin of 10% but it also outshines adapted ResNet architectures.

A Robust and Precise ConvNet for small non-coding RNA classification (RPC-snRC)