Publication
A Robust and Precise ConvNet for small non-coding RNA classification (RPC-snRC)
Muhammad Nabeel Asim; Muhammad Imran Malik; Christoph Zehe; Johan Trygg; Andreas Dengel; Sheraz Ahmed
In: IEEE Access, Vol. 9, Pages 19379-19390, IEEE, 2020.
Abstract
Small non-coding RNAs (ncRNAs) are attracting increasing attention as they are now considered potentially valuable resources in the development of new drugs intended to cure several human diseases.
A prerequisite for the development of drugs targeting ncRNAs or the related pathways is the identification
and correct classification of such ncRNAs. State-of-the-art small ncRNA classification methodologies
use secondary structural features as input. However, such feature extraction approaches only take global
characteristics into account and completely ignore co-relative effects of local structures. Furthermore,
secondary structure based approaches incorporate high dimensional feature space which is computationally
expensive. The present paper proposes a novel Robust and Precise ConvNet (RPC-snRC) methodology which
classifies small ncRNAs into relevant families by utilizing their primary sequence. RPC-snRC methodology
learns hierarchical representation of features by utilizing positioning and information on the occurrence 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 ncRNA classification, we also adapted two ResNet architectures having
a different number of layers. Experimental results on a benchmark small ncRNA dataset show that the
proposed methodology does not only outperform existing small ncRNA classification approaches with a
significant performance margin of 10% but it also gives better results than adapted ResNet architectures.
To reproduce the results Source code and data set is available at https://github.com/muas16/small-noncoding-RNA-classification