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Our group focuses on developing innovative and efficient methods to analyze large DNA and RNA sequence data sets in order to provide a genome-scale understanding of cellular function. Our research contributions integrate sophisticated machine learning techniques and statistical methods for identifying stretches of genomic DNA that have a biological function.This includes protein-coding genes, noncoding RNA genes, and regulatory regions that control the genes. A huge and still-growing number of genetic studies depend on accurate and complete gene descriptions, so our current research effort continues to be dedicated towards both the creation of computational pipelines that will improve the gene annotation as well as in maintaining the existing gene catalogues.
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bioRxiv : the preprint server for biology (2023)
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bioRxiv : the preprint server for biology (2023)
bioRxivno. 25 (2022): 22-23
biorxiv(2022)
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