Research Paper on DNA-SEnet: A Convolutional Neural Network for Classifying DNA-Asthma Associations
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Asthma is a complex disease with a growing global prevalence, and whose genetic causes remain largely unexplored. The rise of next-generation sequencing has significantly augmented genetic studies in identifying asthma-associated mutations, the most common of which are single nucleotide polymorphisms (SNPs). However, SNPs alone do not explain the mechanisms of asthma, nor do they offer a context to evaluate candidate SNP-asthma associations. To this end, Siva developed a model named DNA Sequence Embedding Network (DNA-SEnet) to classify DNA-asthma associations using their genomic patterns. The hypotheses of this study suggested that DNA-asthma associations can be discerned through high-dimensional vector representations of DNA sequences around SNPs, which can be applied to determine novel SNP-asthma associations. Siva's model can also be used to identify novel disease-associated sequences across various disease types.
Siva wrote a paper that was published in the Journal of Emerging Investigators.Read his paper