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Deep Learning Applied to Genomics

Ariel Schwartz, Director of Computational Biology, Synthetic Genomics Zach Dwiel, Senior Data Scientist, Intel AI Lab The field of genomics produces huge volumes of DNA and protein sequence data; annotating each sequence with the correct meta-data, filtering and searching for sequences based on user specified labels is a crucial aspect of research in this domain. However, traditional annotation methods are too slow; a fast and reliable tool that is easily accessible to researchers can greatly accelerate innovation. Towards this objective, Synthetic Genomics Incorporated (SGI) worked with Intel to conduct a deep learning proof of concept that would automatically tag protein sequences. In this talk, SGI and Intel researchers will talk about how this collaboration leveraged Intel’s AI capabilities to automate SGI’s protein sequence analysis to run in mere seconds. The resulting modeling framework allowed the data scientists at SGI to utilize the large amount of existing data and extract new insights that traditional methods couldn’t provide.