  Functional or non-coding RNAs are attracting more attention as they are now
potentially considered valuable resources in the development of new drugs
intended to cure several human diseases. The identification of drugs targeting
the regulatory circuits of functional RNAs depends on knowing its family, a
task which is known as RNA sequence classification. State-of-the-art small
noncoding RNA classification methodologies take secondary structural features
as input. However, in such classification, feature extraction approaches only
take global characteristics into account and completely oversight co-relative
effect of local structures. Furthermore secondary structure based approaches
incorporate high dimensional feature space which proves computationally
expensive. This paper proposes a novel Robust and Precise ConvNet (RPC-snRC)
methodology which classifies small non-coding RNAs sequences into their
relevant families by utilizing the primary sequence of RNAs. RPC-snRC
methodology learns hierarchical representation of features by utilizing
positioning and occurrences information of nucleotides. To avoid exploding and
vanishing gradient problems, we use an approach similar to DenseNet in which
gradient can flow straight from subsequent layers to previous layers. In order
to assess the effectiveness of deeper architectures for small non-coding RNA
classification, we also adapted two ResNet architectures having different
number of layers. Experimental results on a benchmark small non-coding RNA
dataset show that our proposed methodology does not only outperform existing
small non-coding RNA classification approaches with a significant performance
margin of 10% but it also outshines adapted ResNet architectures.
