BioAutoMATED is an automated machine learning (AutoML) platform for biological research that integrates multiple AutoML methods into a unified framework. An open source system, BioAutoMATED helps researchers who want to use machine learning for biology reduce a months-long process of data model selection and processing to just a few hours.
Automated machine-learning (AutoML) algorithms can address many of the challenges that come with applying ML to life sciences. However, these algorithms are rarely used in systems and synthetic biology studies because they typically do not explicitly handle biological sequences—e.g., nucleotide, amino acid, or glycan sequences— and cannot be easily compared with other AutoML algorithms.
Developed and presented by Jim Collins, faculty lead for life sciences at the Jameel Clinic and colleagues at the Massachusetts Institute of Technology (MIT), users of BioAutoMATED are automatically provided with relevant techniques for analysing, interpreting and designing biological sequences. BioAutoMATED predicts gene regulation, peptide-drug interactions and glycan annotation, and designs optimised synthetic biology components, revealing salient sequence characteristics. By automating sequence modelling, BioAutoMATED allows life scientists to incorporate ML more readily into their work.