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- Title
ProQ3D: improved model quality assessments using deep learning.
- Authors
Uziela, Karolis; Menéndez Hurtado, David; Nanjiang Shu; Wallner, Björn; Elofsson, Arne
- Abstract
Summary: Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and 0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a protein. Here, we show that a substantial improvement can be obtained using exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector machine by a deep neural network. This improves the Pearson correlation to 0.90 (0.85 using ProQ2 input features).
- Subjects
PROTEINS; BIOINFORMATICS; MACHINE learning; ARTIFICIAL neural networks; PEARSON correlation (Statistics)
- Publication
Bioinformatics, 2017, Vol 33, Issue 10, p1578
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btw819