“The fundamental language of biology is based on sequences,” explained Soenksen, who earned his doctorate in the MIT Department of Mechanical Engineering. “Biological sequences such as DNA, RNA, proteins, and glycans have the amazing informational property of being intrinsically standardized, like an alphabet. A lot of AutoML tools are developed for text, so it made sense to extend it to [biological] sequences.
“But you can’t really know from the start of a project which model will be best for your dataset,” Valeri said. “By incorporating multiple tools under one umbrella tool, we really allow a much larger search space than any individual AutoML tool could achieve on its own.” "Our tool explores models that are better-suited for smaller, sparser biological datasets as well as more complex neural networks,” Valeri said. This is particularly well suited to research groups with new data that may or may not be suited for a machine learning problem.
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