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Research Paper on DNA-SEnet: A Convolutional Neural Network for Classifying DNA-Asthma Associations

Siva is an 18 year-old high schooler from Scottsdale, AZ.
High School
BASIS Scottsdale
Graduation Year
Student review
Polygence has been the most amazing research experience I've had the opportunity to be in. It allowed me to develop a personal project centered around my academic passions and pursue them with the end goal of tackling a contemporary global issue. As I conducted my research, I developed new academic skills (such as coding and machine learning in R) and refined other existing skills (such as analytical reading and writing). Moreover, Polygence gave me the opportunity to showcase my work through a publication, which helped me apply and gain admission to some of my top choice colleges. Ultimately, I'm most thankful for my mentor, Brianna. The 1-1 mentorship that Polygence offers allowed me to gain practical experience from an accomplished researcher in my field of interest. While I was performing my research, Brianna always gave me ways to improve my current methodology and resources on where we could look to accomplish our next steps. Overall, she helped me refine my research interests as I transitioned into college and told me about potential career options I could pursue. I'm incredibly grateful for the opportunity Polygence gave me and I highly recommend it to other motivated high school students.

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Project description

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.

Research Paper on DNA-SEnet: A Convolutional Neural Network for Classifying DNA-Asthma Associations
Project outcome

Siva wrote a paper that was published in the Journal of Emerging Investigators.

Read his paper

Doctor of Philosophy
Computational Genomics, Genetics (regular), Engineering, AI/ML
Bioengineering, biomedical data science, genetics, computational biology, computer science, machine learning

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