AI Based Robust DNA Motif Discovery and Classification

DNAInsight, a unified computational platform that integrates deep neural networks for DNA sequence classification with motif discovery algorithms to identify functional elements in genomic sequences.The rapid growth of genomic data requires efficient computational methods for sequence classification and the discovery of regulatory elements.Our system employs k-mer frequency-based feature extraction combined with a deep neural network design to obtain resilient DNA sequence classification across a wide range of taxonomic groups.We illustrate how this method effectively extracts sequence composition patterns without involving intricate alignment protocols.DNAInsight also incorporates two complementary motif discovery methods Expectation-Maximization (EM) and Gibbs sampling to detect conserved sequence patterns that can be indicative of transcription factor binding sites or other functional features.This study responds to the urgent requirement for combined tools that span sequence-level pattern identification and functional element detection, providing information on genomic structure-function correlations.The open-source aspect of DNAInsight promotes community-driven refinements and modifications for given research environments

Publication Details

Journal
International Research Journal of Modernization in Engineering Technology and Science
Published
2025-10-04
DOI
https://doi.org/10.56726/irjmets81037
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

AI Based Robust DNA Motif Discovery and Classification

International Research Journal of Modernization in Engineering Technology and Science
Genomics and Phylogenetic Studies
article

AI Based Robust DNA Motif Discovery and Classification

article en

Abstract

DNAInsight, a unified computational platform that integrates deep neural networks for DNA sequence classification with motif discovery algorithms to identify functional elements in genomic sequences.The rapid growth of genomic data requires efficient computational methods for sequence classification and the discovery of regulatory elements.Our system employs k-mer frequency-based feature extraction combined with a deep neural network design to obtain resilient DNA sequence classification across a wide range of taxonomic groups.We illustrate how this method effectively extracts sequence composition patterns without involving intricate alignment protocols.DNAInsight also incorporates two complementary motif discovery methods Expectation-Maximization (EM) and Gibbs sampling to detect conserved sequence patterns that can be indicative of transcription factor binding sites or other functional features.This study responds to the urgent requirement for combined tools that span sequence-level pattern identification and functional element detection, providing information on genomic structure-function correlations.The open-source aspect of DNAInsight promotes community-driven refinements and modifications for given research environments

International Research Journal of Modernization in Engineering Technology and Science
Openalex Percentile: Top 76%
Genomics and Phylogenetic Studies
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