ProteInfer is a model for protein function prediction that is trained to predict the functional properties of protein sequences using Deep Learning.
Authors provide pre-trained models for two tasks: Gene Ontology (GO) and Enzyme Commission (EC) number prediction, as well as two data splits two data splits: random and clustered.
Additionally, for every task and data split combination, authors trained multiple models using different random seeds.
This model is trained on the GO task with the RANDOM data split, and corresponds to the model with ID 13731645 in the original ProteInfer repository.
This model is intended for research use. It can be used for protein function prediction tasks, such as Gene Ontology (GO) and Enzyme Commission (EC) number prediction, or
as a feature extractor for protein sequences.
Downstream Use
This model can be fine-tuned for any task that can benefit from function-aware protein embeddings.
Bias, Risks, and Limitations
This model is intended for use on protein sequences. It is not meant for other biological sequences, such as DNA sequences.
How to Get Started with the Model
git clone https://github.com/samirchar/proteinfertorch
cd proteinfertorch
conda env create -f environment.yml
conda activate proteinfertorch
pip install -e ./ # make sure ./ is the dir including setup.py
For detailed instructions on package usage, please refer to the README in model repo
Evaluation
Results
TODO: Add table comparing the performance of this model with the original TensorFlow 1.X implementation.
Technical Specifications
Compute Infrastructure
8xV100 GPU cluster
Citation
BibTeX:
If you use this model in your work, I would greatly appreciate it if you could cite it as follows:
