多类信号肽预测和结构解码模型。_Python_Jupyter Notebook_下载.zip

# SignalP 6.0 Signal peptide prediction model based on a [Bert protein language model encoder](https://github.com/agemagician/ProtTrans) and a conditional random field (CRF) decoder. This is the development codebase. If you are looking for the prediction service, go to https://services.healthtech.dtu.dk/service.php?SignalP-6.0. The installation instructions for the installable SignalP 6.0 prediction tool can be found [here](https://github.com/fteufel/signalp-6.0/blob/main/installation_instructions.md). Install in editable mode with `pip install -e ./` to experiment. ## Data The training dataset as well as the full dataset before homology partitioning are in `data`. The directory additionally contains the extended vocabulary of the ProtTrans `BertTokenizer` used. ## Training You can find the training script in `scripts/train_model.py`. The pytorch model is in `src/signalp6/models`. Please refer to the training script source for the meaning of all parameters. A basic training command looks like this: ``` python3 scripts/train_model.py --data data/train_set.fasta --test_partition 0 --validation_partition 1 --output_dir testruns --experiment_name testrun1 --remove_top_layers 1 --kingdom_as_token --sp_region_labels --region_regularization_alpha 0.5 --constrain_crf --average_per_kingdom ``` ## Other things in package - `training_utils` contains parts that were used to fit the model, e.g. dataloading and regularization. - `utils` contains other utilities, such as functions to calculate metrics or region statistics.