Background: Molecular biomarkers show new ways to understand many disease
processes. Noncoding RNAs as biomarkers play a crucial role in several cellular activities, which
are highly correlated to many human diseases especially cancer. The classification and the
identification of ncRNAs have become a critical issue due to their application, such as biomarkers
in many human diseases.
Objective: Most existing computational tools for ncRNA classification are mainly used for
classifying only one type of ncRNA. They are based on structural information or specific known
features. Furthermore, these tools suffer from a lack of significant and validated features.
Therefore, the performance of these methods is not always satisfactory.
Methods: We propose a novel approach named imCnC for ncRNA classification based on
multisource deep learning, which integrates several data sources such as genomic and epigenomic
data to identify several ncRNA types. Also, we propose an optimization technique to visualize the
extracted features pattern from the multisource CNN model to measure the epigenomics features
of each ncRNA type.
Results: The computational results using a dataset of 16 human ncRNA classes downloaded from
RFAM show that imCnC outperforms the existing tools. Indeed, imCnC achieved an accuracy of
94,18%. In addition, our method enables to discover new ncRNA features using an optimization
technique to measure and visualize the features pattern of the imCnC classifier.