Robust Transcription Factor Binding Site Prediction Using Deep Neural Networks | Bentham Science

摘要

Aims: Robust and more accurate method for identifying transcription factor binding sites(TFBS) for gene expressionBackground: Deep neural networks (DNNs) have shown promising growth in solving complexmachine learning problems. Conventional techniques are comfortably replaced by DNNs incomputer vision, signal processing, healthcare, and genomics. Understanding DNA sequences isalways a crucial task in healthcare and regulatory genomics. For DNA motif prediction, choosing theright dataset with a sufficient number of input sequences is crucial in order to design an effectivemodel.Objective: Designing a new algorithm which works on different dataset while an improvedperformance for TFBS predictionMethods: With the help of Layerwise Relevance Propagation, the proposed algorithm identifies theinvariant features with adaptive noise patterns.Results: The performance is compared by calculating various metrics on standard as well as recentmethods and significant improvement is noted.Conclusion: By identifying the invariant and robust features in the DNA sequences, theclassification performance can be increased.