Year: 2022
Type: dissertation
Abstract: Predicting protein function and structure from sequence is one important challenge for computational biology. For nearly 3 decades, most approaches that took up on this challenge combined machine learning and evolutionary information (EI). Here, I introduced novel concepts from representation learning to protein sequences to bypass some of the limitations inherent to EI. The proposed solution rivals EI-based methods in their predictive performance while relying only on single protein sequences.
Authors: Heinzinger, Michael, Rost, Burkhard, Technische Universität München
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: G1270295441, G505595760
OpenAlex ID: W7057203522