We want to lower these barriers for domain experts in biology.” With BioAutoMATED, researchers have the freedom to run initial experiments to assess if it’s worthwhile to hire a machine-learning expert to build a different model for further experimentation. "Currently, biology-centric labs need to invest in significant digital infrastructure and AI-ML trained human resources before they can even see if their ideas are poised to pan out. "Conducting novel and successful experiments at the intersection of biology and machine learning can cost a lot of money,” Soenksen explains. This is an advantage for research groups with new data that may or may not be suited for a machine learning problem.
"Our tool explores models that are better-suited for smaller, sparser biological datasets as well as more complex neural networks,” Valeri says. BioAutoMATED is even able to help determine how much data is required to appropriately train the chosen model. “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.”īioAutoMATED’s repertoire of supervised ML models includes three types: binary classification models (dividing data into two classes), multi-class classification models (dividing data into multiple classes), and regression models (fitting continuous numerical values or measuring the strength of key relationships between variables).
“But you can’t really know from the start of a project which model will be best for your dataset,” Valeri says. Moreover, most AutoML tools can only explore and build reduced types of models. A lot of AutoML tools are developed for text, so it made sense to extend it to sequences.” “Biological sequences such as DNA, RNA, proteins, and glycans have the amazing informational property of being intrinsically standardized, like an alphabet. “The fundamental language of biology is based on sequences,” explains Soenksen, who earned his doctorate in the MIT Department of Mechanical Engineering. Automated machine-learning (AutoML) systems are still in a relatively nascent stage of development, with current usage primarily focused on image and text recognition, but largely unused in subfields of biology, points out first co-author and Jameel Clinic postdoc Luis Soenksen PhD '20. “It would take many weeks of effort to figure out the appropriate model for our dataset, and this is a really prohibitive step for a lot of folks that want to use machine learning or biology,” says Jacqueline Valeri, a fifth-year PhD student of biological engineering in Collins’s lab who is first co-author of the paper.īioAutoMATED is an automated machine-learning system that can select and build an appropriate model for a given dataset and even take care of the laborious task of data preprocessing, whittling down a months-long process to just a few hours.
The two choices offered are either “Less than half the project time” or “More than half the project time.” If you guessed the latter, you would be correct Google states that it takes over 80 percent of project time to format the data, and that’s not even taking into account the time needed to frame the problem in machine-learning terms. “In your machine-learning project, how much time will you typically spend on data preparation and transformation?” asks a 2022 Google course on the Foundations of Machine Learning (ML).
Even with a machine-learning expert, selecting the appropriate model, formatting the dataset for the model, then fine-tuning it can dramatically change how the model performs, and takes a lot of work. Recruiting machine-learning researchers can be a time-consuming and financially costly process for science and engineering labs. An open-access paper on their proposed solution, called BioAutoMATED, was published on June 21 in Cell Systems. Jim Collins, the Termeer Professor of Medical Engineering and Science in the Department of Biological Engineering at MIT and the life sciences faculty lead at the Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), along with a number of colleagues decided to tackle this problem when facing a similar conundrum. Is it possible to build machine-learning models without machine-learning expertise?
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