slives-lab/bioautomated_ai

Automated machine learning for analyzing, interpreting, and designing biological sequences

BioAutoMATED

an end-to-end automated machine learning tool for explanation and design of biological sequences (Valeri et al. 2023)

Abstract

The design choices underlying machine learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine learning (AutoML) algorithms can address many design challenges that come with applying ML to the life sciences. However, these algorithms are rarely used in systems 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. Here, we present BioAutoMATED, an AutoML platform for biological sequence analysis that integrates multiple AutoML methods into a unified framework. Users are automatically provided with relevant techniques for analyzing, interpreting, and designing biological sequences. BioAutoMATED predicts gene regulation, peptide-drug interactions, and glycan annotation with performance comparable to that of manually tuned models, revealing salient sequence characteristics. By automating sequence modeling, BioAutoMATED allows life scientists to more readily incorporate ML into their work.

Installation Instructions

Please find all installation instructions, for both GitHub and DockerHub installations, in the provided Installation Guide file.

Feel free to reach out to jackievaleri8 "at" gmail "dot" com with questions.