ProteInfer, deep neural networks for protein functional inference
The Francis Crick Institute · Google (United States) · +1 more institution
Abstract
Predicting the function of a protein from its amino acid sequence is a long-standing challenge in bioinformatics. Traditional approaches use sequence alignment to compare a query sequence either to thousands of models of protein families or to large databases of individual protein sequences. Here we introduce ProteInfer, which instead employs deep convolutional neural networks to directly predict a variety of protein functions - Enzyme Commission (EC) numbers and Gene Ontology (GO) terms - directly from an unaligned amino acid sequence. This approach provides precise predictions which complement alignment-based methods, and the computational efficiency of a single neural network permits novel and lightweight…
Citation impact
- FWCI
- 26.17
- Percentile
- 100%
- References
- 84
Authors
4Topics & keywords
- Computer science
- Convolutional neural network
- Complement (music)
- Inference
- Artificial neural network
- Sequence (biology)
- Protein structure prediction
- Artificial intelligence