- author = "Jacqueline A. Valeri and Luis R. Soenksen and Katherine M. Collins and Pradeep Ramesh and George Cai and Rani Powers and Nicolaas M. Angenent-Mari and Diogo M. Camacho and Felix Wong and Timothy K. Lu and James J. Collins",
- title = "{BioAutoMATED}: An end-to-end automated machine learning tool for explanation and design of biological sequences",
- journal = "Cell Systems",
- year = "2023",
- volume = "14",
- number = "6",
- pages = "525--542.e9",
- keywords = "genetic algorithms, genetic programming, TPOT, automated machine learning, architecture search, hyperparameter optimization, biological sequences",
- ISSN = "2405-4712",
-
URL = "
https://www.sciencedirect.com/science/article/pii/S2405471223001515",
-
DOI = "
doi:10.1016/j.cels.2023.05.007",
- 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 challenges that come with applying ML to the 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. 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, and designs optimized synthetic biology components, revealing salient sequence characteristics. By automating sequence modeling, BioAutoMATED allows life scientists to incorporate ML more readily into their work.",
-
notes = "also known as \cite{VALERI2023525}
PMID: 37348466
Valeri PhD thesis, Computationally-Guided Discovery and Design of Biological Sequences and Small Molecules, MIT, August 2023",
