Aims: Robust and more accurate method for identifying transcription factor binding sites
(TFBS) for gene expression
Background: Deep neural networks (DNNs) have shown promising growth in solving complex
machine learning problems. Conventional techniques are comfortably replaced by DNNs in
computer vision, signal processing, healthcare, and genomics. Understanding DNA sequences is
always a crucial task in healthcare and regulatory genomics. For DNA motif prediction, choosing the
right dataset with a sufficient number of input sequences is crucial in order to design an effective
Objective: Designing a new algorithm which works on different dataset while an improved
performance for TFBS prediction
Methods: With the help of Layerwise Relevance Propagation, the proposed algorithm identifies the
invariant features with adaptive noise patterns.
Results: The performance is compared by calculating various metrics on standard as well as recent
methods and significant improvement is noted.
Conclusion: By identifying the invariant and robust features in the DNA sequences, the
classification performance can be increased.
Keywords: Deep neural networks, transcription factor binding sites, regularization, DNA, RNA, convolutional neural
Rights & PermissionsPrintExport