Original paper
Learned protein embeddings for machine learning
Abstract
Motivation Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enable the prediction and discovery of sequences with optimal properties. Machine-learning models generally require that their inputs be vectors, and the conversion from a protein sequence to a vector...
Paper Details
Title
Learned protein embeddings for machine learning
Published Date
Mar 23, 2018
Journal
Volume
34
Issue
15
Pages
2642 - 2648
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Notes
History