Title
i6mA-Caps: a CapsuleNet-based framework for identifying DNA N6-methyladenine sites
Abstract
Motivation: DNA N6-methyladenine (6mA) has been demonstrated to have an essential function in epigenetic modification in eukaryotic species in recent research. 6mA has been linked to various biological processes. It's critical to create a new algorithm that can rapidly and reliably detect 6mA sites in genomes to investigate their biological roles. The identification of 6mA marks in the genome is the first and most important step in understanding the underlying molecular processes, as well as their regulatory functions. Results: In this article, we proposed a novel computational tool called i6mA-Caps which CapsuleNet based a framework for identifying the DNA N6-methyladenine sites. The proposed framework uses a single encoding scheme for numerical representation of the DNA sequence. The numerical data is then used by the set of convolution layers to extract low-level features. These features are then used by the capsule network to extract intermediate-level and later high-level features to classify the 6mA sites. The proposed network is evaluated on three datasets belonging to three genomes which are Rosaceae, Rice and Arabidopsis thaliana. Proposed method has attained an accuracy of 96.71%, 94% and 86.83% for independent Rosaceae dataset, Rice dataset and A.thaliana dataset respectively. The proposed framework has exhibited improved results when compared with the existing top-of-the-line methods.
Year
DOI
Venue
2022
10.1093/bioinformatics/btac434
BIOINFORMATICS
DocType
Volume
Issue
Journal
38
16
ISSN
Citations 
PageRank 
1367-4803
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Mobeen Ur Rehman111.06
Hilal Tayara200.34
quan zou355867.61
Kil To Chong400.68