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TransPRSFP: Machine Translation-Based Protein Region-Specific Function Prediction

Knowledge regarding the functions carried out by proteins are essential to understand biological mechanisms and to enable effective disease research. The gene ontology (GO) system is utilised for annotating gene/protein functions. Proteins are modular and composed of structural and functional blocks called domains. Many proteins are multi-domain, which have multiple functions. These functions are localised to certain regions on the sequence/structure. The existing research on protein function prediction focuses on protein level annotations (i.e., predicting a function for the whole sequence), ignoring which part of the protein is responsible for the function of interest. The aim of this study is to develop a deep learning-based method to predict protein functions at the residue level, as well as protein level. Protein level predictions are conducted by our classification model which takes ProtT5-XL-UniRef50 language model embeddings of protein sequences as input and predicts their GO term-basedfunctions via running them through a series of linear layers (feed forward network) (Figure 1A). For residue level (region specific) predictions, which constitutes our main novelty, we adopted the machine translation approach in natural language processing and utilised transformer decoder blocks which takes ProtT5_XL sequence embeddings (i.e., input sentences in the source language) at the residue level together with GO term embeddings (i.e., sentences in the target language) and predicts a GO term for each input residue, meaning that the residue of interest belong to a region which carries out the predicted function (Figure 1B). Finally, we created a hybrid model that directly merges predictions from both models (Figure 1C). As training and test datasets, we employed protein level GO annotations from UniProt-GOA and residue/domain level annotations from InterPro. Considering model test performances (in terms of average F1-score); residue-level, protein- level, and merged models achieved 0.53, 0.38, and 0.47, respectively. Protein level model had a higher performance; however, adding residue level estimations increased both the coverage and the specificity of predictions.

Figure Figure. Models developed in scope of this research; (A) the feed-forward neural network-based classification model that performs protein-level function prediction; (B) the transformer-based model that performs sequence region level function prediction; and (C) the hybrid model constitutes classification and transformer-based models together to increase function prediction coverage and specificity.

The Python script provided in this repository makes it possible to train both types of models and perform predictions using pre-trained models.

Getting Started

The recommended Python version is 3.8.10 since the script has been tested by using this version.

To create a clean virtual environment, use the following command:

$ python3 -m venv virtual_env

Then, activate the virtual environment by using the following command:

$ source virtual_env/bin/activate

After activating the virtual environment, in the directory where the requirements.txt file resides, execute the following command to install the requirements:

$ pip install -r requirements.txt

Usage

The script has the following main usages:

  • Training the classification head model on non-regional datasets
  • Training the transformer model on regional datasets
  • Running a pre-trained classification head model in inference mode
  • Running a pre-trained transformer model in inference mode
  • Running a pre-trained classification head model and a pre-trained transformer model together in inference mode to produce both regional and non-regional predictions.

Note: For each of the main usages specified above, the ProtT5-XL-UniRef50 encoder model is needed. You can download it using the following link: https://drive.google.com/file/d/1PnPtgdzkopjdjNS6XMLhjyS9bioMT2Kq/view?usp=sharing

You can extract the encoder model folder from the archive using the following command:

$ tar xvf Rostlab_prot_t5_xl_uniref50.tar.xz

There is a section below for each of the main uses listed above.

For seeing the usage of the script, the following command can be executed:

$ python3 main.py -h

The output of this command is as follows:

usage: main.py [-h] [-i] [-t] [-d DEVICE] -dp DATASET -mt {transformer,classification_head,merged} [-mp MODEL_PATH] -t5 PROT_T5_MODEL_PATH
               [-th THRESHOLD] [-bs BATCH_SIZE] [-e EPOCH] [-lr LEARNING_RATE] [-msd MODEL_SAVE_DIR] [-spe SAVE_PER_EPOCH] [-ml MAX_LENGTH]
               [-tdr TRAINING_DATASET_RATIO] [-tbld TENSORBOARD_LOG_DIR] [-chmp CLASSIFICATION_HEAD_MODEL_PATH] [-tmp TRANSFORMER_MODEL_PATH]

optional arguments:
  -h, --help            show this help message and exit

arguments:
  -i, --inference       If it is given, the model is set to run in inference mode
  -t, --train           If it is given, the model will be trained
  -d DEVICE, --device DEVICE
                        the device on which the model is to run
  -dp DATASET, --dataset DATASET
                        path to dataset (must be a pickle-saved binary file)
  -mt {transformer,classification_head,merged}, --model-type {transformer,classification_head,merged}
  -mp MODEL_PATH, --model-path MODEL_PATH
                        path to the directory where the model is saved
  -t5 PROT_T5_MODEL_PATH, --prot-t5-model-path PROT_T5_MODEL_PATH
                        path to the directory where ProtT5 model is stored
  -th THRESHOLD, --threshold THRESHOLD
                        threshold for classification head model. The less the threshold, the more the number of false positives, but the less the
                        number of false negatives. The more the threshold, the less the number of false positives, but the more the number of
                        false negatives.
  -bs BATCH_SIZE, --batch-size BATCH_SIZE
                        batch size
  -e EPOCH, --epoch EPOCH
                        number of epochs for training
  -lr LEARNING_RATE, --learning-rate LEARNING_RATE
                        learning rate for training
  -msd MODEL_SAVE_DIR, --model-save-dir MODEL_SAVE_DIR
                        the folder path to save the model(s) (must be non-existent)
  -spe SAVE_PER_EPOCH, --save-per-epoch SAVE_PER_EPOCH
                        if set to a positive value, each x epochs, the model is saved to the model save dir specified in the arguments
  -ml MAX_LENGTH, --max-length MAX_LENGTH
                        the maximum length for a protein sequence. If not set, automatically inferred from the dataset
  -tdr TRAINING_DATASET_RATIO, --training-dataset-ratio TRAINING_DATASET_RATIO
                        the ratio of the number of samples in training data to the number of samples in all dataset (must be between 0 and 1, 0
                        and 1 excluded)
  -tbld TENSORBOARD_LOG_DIR, --tensorboard-log-dir TENSORBOARD_LOG_DIR
                        the folder where the tensorboard logs are to be saved (must be non-existent)
  -chmp CLASSIFICATION_HEAD_MODEL_PATH, --classification-head-model-path CLASSIFICATION_HEAD_MODEL_PATH
                        path to the classification head model (only used in merged model type)
  -tmp TRANSFORMER_MODEL_PATH, --transformer-model-path TRANSFORMER_MODEL_PATH
                        path to the transformer model (only used in merged model type)

Datasets

Models

Dataset Format

  • This script uses the datasets prepared in a specific format and saved as a binary object file using the pickle module.
  • Below, sample datasets consisting of toy examples are given for illustrating the required dataset formats. For viewing the dataset(s), you should download it and use the built-in pickle module in Python as follows (You can first open a Python shell and execute the following lines): 
    >>> import pickle
>>> with open("test_nonregional_dataset.pkl", "rb") as f:
>>>     dataset = pickle.load(f)
>>> dataset
  • To examine the dataset format for training the classification head model, please use the following link: test_nonregional_dataset.pkl
  • To examine the dataset format for training the transformer model, please use the following link: test_regional_dataset.pkl
  • To examine the dataset format for running the model(s) in inference mode, please use the following link: test_dataset.pkl. Note that the formats of the dataset files named as test_nonregional_dataset.pkl and test_regional_dataset.pkl are also applicable for running the model(s) in inference mode. In the case where the GO term annotations are not known, the format of the file test_dataset.pkl must be used.

For getting acquainted with the usage of the script, it is recommended that you download the sample datasets specified above and use them to test the script by using the commands specified below.

Using the Script to Train the Classification Head Model

A sample command for using the script to train the classification head model is as follows:

$ python3 main.py --train --device cuda:0 --dataset ../test_nonregional_dataset.pkl --model-type classification_head -t5 ../Rostlab_prot_t5_xl_uniref50 --model-save-dir ../classification_head_model_test --batch-size 8 --epoch 150 --learning-rate 0.005 --training-dataset-ratio 0.8

Executing this script by using the command above means that the classification head model will be trained

  • by using the device cuda:0
  • on the dataset found at relative path ../test_nonregional_dataset.pkl
  • by using the T5Encoder model found at relative path ../Rostlab_prot_t5_xl_uniref50
  • by saving the model at relative path ../classification_head_model_test at the end of the training
  • by using the batch size as 8
  • for 150 epochs
  • by using the learning rate as 0.005
  • by using 80% of the dataset as the training set, while the rest being the validation set.

Using the Script to Train the Transformer Model

A sample command for using the script to train the transformer model is as follows:

$ python3 main.py --train --device cuda:0 --dataset ../test_regional_dataset.pkl -mt transformer -t5 ../Rostlab_prot_t5_xl_uniref50 --model-save-dir ../transformer_model_test --batch-size 8 --epoch 150 --learning-rate 0.05 --training-dataset-ratio 0.8 --tensorboard-log-dir ../transformer_model_test_tensorboard

Executing this script by using the command above means that the transformer model will be trained

  • by using the device cuda:0
  • on the dataset found at relative path ../test_regional_dataset.pkl
  • by using the T5Encoder model found at relative path ../Rostlab_prot_t5_xl_uniref50
  • by saving the model at relative path ../transformer_model_test at the end of the training
  • by using the batch size as 8
  • for 150 epochs
  • by using the learning rate as 0.05
  • by using 80% of the dataset as the training set, while the rest being the validation set
  • by saving tensorboard logs during training to the relative path ../transformer_model_test_tensorboard

Using the Script to Run the Classification Head Model in Inference Mode

A sample command for using the script to run the classification head model in inference mode is as follows:

$ python3 main.py --inference --model-type classification_head --model-path ../classification_head_model_test/end_of_training/ --threshold 0.20 --device cuda:0 -t5 ../Rostlab_prot_t5_xl_uniref50 --dataset ../test_dataset.pkl

Using the command above means that the classification head model found at relative path ../classification_head_model_test/end_of_training/ will be executed in inference mode

  • by using the device cuda:0
  • by using the threshold 0.20
  • by using the T5Encoder model found at relative path ../Rostlab_prot_t5_xl_uniref50
  • on the dataset found at relative path ../test_dataset.pkl

The sample outputs produced by the command above is as follows:

2023-06-17 07:04:57.145896: Loading ProtT5 tokenizer...
2023-06-17 07:04:57.716089: Done!
2023-06-17 07:04:57.716139: Loading ProtT5 encoder...
2023-06-17 07:05:09.797326: Done!
2023-06-17 07:05:09.797427: Loading classification model...
2023-06-17 07:05:17.368933: Done!
2023-06-17 07:05:18.234049: G: GO:0000001 GO:0000004 GO:0000005 GO:0000008 GO:0000009
2023-06-17 07:05:18.257649: QDRSMEN: GO:0000002 GO:0000004 GO:0000008
2023-06-17 07:05:18.280082: WLQT: GO:0000001 GO:0000003 GO:0000004 GO:0000009
2023-06-17 07:05:18.302514: W: GO:0000001 GO:0000004 GO:0000005 GO:0000008 GO:0000009 GO:0000010
2023-06-17 07:05:18.324971: KRD: GO:0000004 GO:0000009 GO:0000010
2023-06-17 07:05:18.347703: EVHGMEKGMI: GO:0000002 GO:0000003 GO:0000004 GO:0000007 GO:0000010
2023-06-17 07:05:18.371884: HKSTVWS: GO:0000001 GO:0000002 GO:0000004 GO:0000009 GO:0000010
2023-06-17 07:05:18.394303: DSTILAC: GO:0000003 GO:0000004 GO:0000009 GO:0000010
2023-06-17 07:05:18.419012: MEQGECPLMR: GO:0000002 GO:0000003 GO:0000004 GO:0000005 GO:0000006 GO:0000008 GO:0000010
2023-06-17 07:05:18.441450: AK: GO:0000002 GO:0000003 GO:0000004 GO:0000009 GO:0000010
2023-06-17 07:05:18.463799: FMAVREVLGH: GO:0000001 GO:0000004 GO:0000010
2023-06-17 07:05:18.486209: DALIDHWW: GO:0000003 GO:0000004 GO:0000007 GO:0000009 GO:0000010
2023-06-17 07:05:18.512298: QTQYN: GO:0000002 GO:0000004 GO:0000007 GO:0000008
2023-06-17 07:05:18.534839: G: GO:0000001 GO:0000004 GO:0000005 GO:0000008 GO:0000009
2023-06-17 07:05:18.557256: IQLHWCAAA: GO:0000003 GO:0000004 GO:0000006 GO:0000009 GO:0000010
2023-06-17 07:05:18.579784: T: GO:0000001 GO:0000004 GO:0000009
2023-06-17 07:05:18.603728: TGERVTPM: GO:0000003 GO:0000004 GO:0000007 GO:0000008 GO:0000009 GO:0000010
2023-06-17 07:05:18.625935: MITP: GO:0000001 GO:0000002 GO:0000003 GO:0000004 GO:0000005
2023-06-17 07:05:18.651647: LYDF: GO:0000003 GO:0000004 GO:0000010
2023-06-17 07:05:18.675184: LDGNCHFL: GO:0000002 GO:0000004 GO:0000008 GO:0000009 GO:0000010
2023-06-17 07:05:18.698682: P: GO:0000001 GO:0000004 GO:0000005 GO:0000008 GO:0000009
2023-06-17 07:05:18.721636: DAHF: GO:0000001 GO:0000002 GO:0000003 GO:0000004 GO:0000005 GO:0000007 GO:0000009
2023-06-17 07:05:18.745697: MCWDIS: GO:0000002 GO:0000003 GO:0000005 GO:0000006 GO:0000010
2023-06-17 07:05:18.770178: RGM: GO:0000003 GO:0000004 GO:0000005 GO:0000007 GO:0000008 GO:0000009 GO:0000010
2023-06-17 07:05:18.794179: NNTHNH: GO:0000004 GO:0000007 GO:0000008 GO:0000010
2023-06-17 07:05:18.816725: ML: GO:0000002 GO:0000003 GO:0000005 GO:0000006 GO:0000008 GO:0000010
2023-06-17 07:05:18.838899: YYCGCHIG: GO:0000001 GO:0000002 GO:0000010
2023-06-17 07:05:18.861319: KVLTI: GO:0000004 GO:0000005 GO:0000007 GO:0000010
2023-06-17 07:05:18.883900: R: GO:0000001 GO:0000004 GO:0000005 GO:0000009 GO:0000010
2023-06-17 07:05:18.908037: VNY: GO:0000002 GO:0000003 GO:0000004
2023-06-17 07:05:18.931543: LPN: GO:0000003 GO:0000004 GO:0000008
2023-06-17 07:05:18.954073: TKGPNDNA: GO:0000003 GO:0000004 GO:0000009

Using the Script to Run the Transformer Model in Inference Mode

A sample command for using the script to run the transformer model in inference mode is as follows:

$ python3 main.py --inference --model-type transformer --model-path ../transformer_model_test/embed_size_1024.max_length_10000.lr_0.05_end_of_training/ --device cuda:0 -t5 ../Rostlab_prot_t5_xl_uniref50 --dataset ../test_dataset.pkl

Using the command above means that the transformer model found at relative path ../transformer_model_test/embed_size_1024.max_length_10000.lr_0.05_end_of_training/ will be executed in inference mode

  • by using the device cuda:0
  • by using the T5Encoder model found at relative path ../Rostlab_prot_t5_xl_uniref50
  • on the dataset found at relative path ../test_dataset.pkl

The sample outputs produced by the command above is as follows:

2023-06-17 07:19:01.558596: Loading ProtT5 tokenizer...
2023-06-17 07:19:02.127968: Done!
2023-06-17 07:19:02.128011: Loading ProtT5 encoder...
2023-06-17 07:19:14.677408: Done!
2023-06-17 07:19:14.677520: Loading transformer model...
2023-06-17 07:19:24.017840: Done!
2023-06-17 07:19:24.866646: G: go:0000010: [[1, 1]] |
2023-06-17 07:19:25.115701: QDRSMEN: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 07:19:25.261224: WLQT: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] |
2023-06-17 07:19:25.319535: W: go:0000010: [[1, 1]] |
2023-06-17 07:19:25.442572: KRD: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] |
2023-06-17 07:19:25.770543: EVHGMEKGMI: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4], [9, 9]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] | go:0000002: [[10, 10]] |
2023-06-17 07:19:26.017410: HKSTVWS: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 07:19:26.260670: DSTILAC: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 07:19:26.611310: MEQGECPLMR: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4], [9, 9]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] | go:0000002: [[10, 10]] |
2023-06-17 07:19:26.708792: AK: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] |
2023-06-17 07:19:27.036395: FMAVREVLGH: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5], [10, 10]] | go:0000007: [[6, 6], [8, 8]] | go:0000003: [[7, 7], [9, 9]] |
2023-06-17 07:19:27.318858: DALIDHWW: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6], [8, 8]] | go:0000003: [[7, 7]] |
2023-06-17 07:19:27.491839: QTQYN: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] |
2023-06-17 07:19:27.546176: G: go:0000010: [[1, 1]] |
2023-06-17 07:19:27.854804: IQLHWCAAA: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6], [8, 8]] | go:0000003: [[7, 7], [9, 9]] |
2023-06-17 07:19:27.918508: T: go:0000010: [[1, 1]] |
2023-06-17 07:19:28.178690: TGERVTPM: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 07:19:28.328460: MITP: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] |
2023-06-17 07:19:28.492370: LYDF: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] |
2023-06-17 07:19:28.756567: LDGNCHFL: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 07:19:28.811352: P: go:0000010: [[1, 1]] |
2023-06-17 07:19:28.973147: DAHF: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] |
2023-06-17 07:19:29.186490: MCWDIS: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] |
2023-06-17 07:19:29.299019: RGM: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] |
2023-06-17 07:19:29.514377: NNTHNH: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] |
2023-06-17 07:19:29.612899: ML: go:0000010: [[1, 1]] | go:0000009: [[2, 2]] |
2023-06-17 07:19:29.876089: YYCGCHIG: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 07:19:30.055239: KVLTI: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] |
2023-06-17 07:19:30.120135: R: go:0000010: [[1, 1]] |
2023-06-17 07:19:30.238388: VNY: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] |
2023-06-17 07:19:30.349400: LPN: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] |
2023-06-17 07:19:30.612022: TKGPNDNA: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6], [8, 8]] | go:0000003: [[7, 7]] |

Using the Script to Run the Merged Model in Inference Mode

A sample command for using the script to run the merged model in inference mode is as follows:

$ python3 main.py --inference --model-type merged --transformer-model-path ../transformer_model_test/embed_size_1024.max_length_10000.lr_0.05_end_of_training/ --classification-head-model-path ../classification_head_model_test/end_of_training/ --threshold 0.20 --device cuda:0 -t5 ../Rostlab_prot_t5_xl_uniref50 --dataset ../test_dataset.pkl

Using the command above means that the merged model consisting of the classification head model found at relative path ../classification_head_model_test/end_of_training/ and the transformer model found at relative path ../transformer_model_test/embed_size_1024.max_length_10000.lr_0.05_end_of_training/ will be executed in inference mode

  • by using the device cuda:0
  • by using the T5Encoder model found at relative path ../Rostlab_prot_t5_xl_uniref50
  • on the dataset found at relative path ../test_dataset.pkl
  • by using the threshold 0.20 for the classification head model.

The sample outputs produced by the command above is as follows:

2023-06-17 04:44:08.914214: Loading ProtT5 tokenizer...
2023-06-17 04:44:09.456218: Done!
2023-06-17 04:44:09.456263: Loading ProtT5 encoder...
2023-06-17 04:44:21.329711: Done!
2023-06-17 04:44:21.334376: Loading classification model...
2023-06-17 04:44:28.720729: Done!
2023-06-17 04:44:29.453718: G: GO:0000001 GO:0000004 GO:0000005 GO:0000008 GO:0000009
2023-06-17 04:44:29.472584: QDRSMEN: GO:0000002 GO:0000004 GO:0000008
2023-06-17 04:44:29.490722: WLQT: GO:0000001 GO:0000003 GO:0000004 GO:0000009
2023-06-17 04:44:29.509122: W: GO:0000001 GO:0000004 GO:0000005 GO:0000008 GO:0000009 GO:0000010
2023-06-17 04:44:29.529871: KRD: GO:0000004 GO:0000009 GO:0000010
2023-06-17 04:44:29.550302: EVHGMEKGMI: GO:0000002 GO:0000003 GO:0000004 GO:0000007 GO:0000010
2023-06-17 04:44:29.570091: HKSTVWS: GO:0000001 GO:0000002 GO:0000004 GO:0000009 GO:0000010
2023-06-17 04:44:29.591253: DSTILAC: GO:0000003 GO:0000004 GO:0000009 GO:0000010
2023-06-17 04:44:29.614257: MEQGECPLMR: GO:0000002 GO:0000003 GO:0000004 GO:0000005 GO:0000006 GO:0000008 GO:0000010
2023-06-17 04:44:29.634674: AK: GO:0000002 GO:0000003 GO:0000004 GO:0000009 GO:0000010
2023-06-17 04:44:29.654129: FMAVREVLGH: GO:0000001 GO:0000004 GO:0000010
2023-06-17 04:44:29.675305: DALIDHWW: GO:0000003 GO:0000004 GO:0000007 GO:0000009 GO:0000010
2023-06-17 04:44:29.696014: QTQYN: GO:0000002 GO:0000004 GO:0000007 GO:0000008
2023-06-17 04:44:29.715333: G: GO:0000001 GO:0000004 GO:0000005 GO:0000008 GO:0000009
2023-06-17 04:44:29.732795: IQLHWCAAA: GO:0000003 GO:0000004 GO:0000006 GO:0000009 GO:0000010
2023-06-17 04:44:29.750773: T: GO:0000001 GO:0000004 GO:0000009
2023-06-17 04:44:29.773830: TGERVTPM: GO:0000003 GO:0000004 GO:0000007 GO:0000008 GO:0000009 GO:0000010
2023-06-17 04:44:29.791398: MITP: GO:0000001 GO:0000002 GO:0000003 GO:0000004 GO:0000005
2023-06-17 04:44:29.809347: LYDF: GO:0000003 GO:0000004 GO:0000010
2023-06-17 04:44:29.827405: LDGNCHFL: GO:0000002 GO:0000004 GO:0000008 GO:0000009 GO:0000010
2023-06-17 04:44:29.845105: P: GO:0000001 GO:0000004 GO:0000005 GO:0000008 GO:0000009
2023-06-17 04:44:29.863337: DAHF: GO:0000001 GO:0000002 GO:0000003 GO:0000004 GO:0000005 GO:0000007 GO:0000009
2023-06-17 04:44:29.882424: MCWDIS: GO:0000002 GO:0000003 GO:0000005 GO:0000006 GO:0000010
2023-06-17 04:44:29.900517: RGM: GO:0000003 GO:0000004 GO:0000005 GO:0000007 GO:0000008 GO:0000009 GO:0000010
2023-06-17 04:44:29.920050: NNTHNH: GO:0000004 GO:0000007 GO:0000008 GO:0000010
2023-06-17 04:44:29.938037: ML: GO:0000002 GO:0000003 GO:0000005 GO:0000006 GO:0000008 GO:0000010
2023-06-17 04:44:29.955522: YYCGCHIG: GO:0000001 GO:0000002 GO:0000010
2023-06-17 04:44:29.973980: KVLTI: GO:0000004 GO:0000005 GO:0000007 GO:0000010
2023-06-17 04:44:29.991912: R: GO:0000001 GO:0000004 GO:0000005 GO:0000009 GO:0000010
2023-06-17 04:44:30.009537: VNY: GO:0000002 GO:0000003 GO:0000004
2023-06-17 04:44:30.027109: LPN: GO:0000003 GO:0000004 GO:0000008
2023-06-17 04:44:30.044768: TKGPNDNA: GO:0000003 GO:0000004 GO:0000009
2023-06-17 04:44:30.438940: Loading transformer model...
2023-06-17 04:44:34.988885: Done!
2023-06-17 04:44:35.053637: G: go:0000010: [[1, 1]] |
2023-06-17 04:44:35.282410: QDRSMEN: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:35.423374: WLQT: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] |
2023-06-17 04:44:35.479875: W: go:0000010: [[1, 1]] |
2023-06-17 04:44:35.591468: KRD: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] |
2023-06-17 04:44:35.904460: EVHGMEKGMI: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4], [9, 9]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] | go:0000002: [[10, 10]] |
2023-06-17 04:44:36.133502: HKSTVWS: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:36.359336: DSTILAC: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:36.672005: MEQGECPLMR: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4], [9, 9]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] | go:0000002: [[10, 10]] |
2023-06-17 04:44:36.755867: AK: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] |
2023-06-17 04:44:37.070720: FMAVREVLGH: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5], [10, 10]] | go:0000007: [[6, 6], [8, 8]] | go:0000003: [[7, 7], [9, 9]] |
2023-06-17 04:44:37.326680: DALIDHWW: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6], [8, 8]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:37.494540: QTQYN: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] |
2023-06-17 04:44:37.548594: G: go:0000010: [[1, 1]] |
2023-06-17 04:44:37.832618: IQLHWCAAA: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6], [8, 8]] | go:0000003: [[7, 7], [9, 9]] |
2023-06-17 04:44:37.886289: T: go:0000010: [[1, 1]] |
2023-06-17 04:44:38.141513: TGERVTPM: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:38.280806: MITP: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] |
2023-06-17 04:44:38.420804: LYDF: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] |
2023-06-17 04:44:38.679136: LDGNCHFL: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:38.732383: P: go:0000010: [[1, 1]] |
2023-06-17 04:44:38.870777: DAHF: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] |
2023-06-17 04:44:39.067616: MCWDIS: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] |
2023-06-17 04:44:39.178638: RGM: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] |
2023-06-17 04:44:39.377095: NNTHNH: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] |
2023-06-17 04:44:39.460283: ML: go:0000010: [[1, 1]] | go:0000009: [[2, 2]] |
2023-06-17 04:44:39.714202: YYCGCHIG: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:39.882665: KVLTI: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] |
2023-06-17 04:44:39.937105: R: go:0000010: [[1, 1]] |
2023-06-17 04:44:40.047229: VNY: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] |
2023-06-17 04:44:40.157041: LPN: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] |
2023-06-17 04:44:40.410786: TKGPNDNA: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6], [8, 8]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:40.410914: G: GO:0000010 GO:0000009 GO:0000008 GO:0000001 GO:0000005 GO:0000004: go:0000010: [[1, 1]] |
2023-06-17 04:44:40.410983: QDRSMEN: GO:0000002 GO:0000009 GO:0000008 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:40.411062: WLQT: GO:0000009 GO:0000001 GO:0000006 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] |
2023-06-17 04:44:40.411103: W: GO:0000010 GO:0000009 GO:0000008 GO:0000001 GO:0000005 GO:0000004: go:0000010: [[1, 1]] |
2023-06-17 04:44:40.411150: KRD: GO:0000010 GO:0000009 GO:0000001 GO:0000006 GO:0000004: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] |
2023-06-17 04:44:40.411204: EVHGMEKGMI: GO:0000002 GO:0000010 GO:0000009 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4], [9, 9]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] | go:0000002: [[10, 10]] |
2023-06-17 04:44:40.411259: HKSTVWS: GO:0000002 GO:0000010 GO:0000009 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:40.411304: DSTILAC: GO:0000010 GO:0000009 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:40.411364: MEQGECPLMR: GO:0000002 GO:0000010 GO:0000009 GO:0000008 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4], [9, 9]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] | go:0000002: [[10, 10]] |
2023-06-17 04:44:40.411395: AK: GO:0000002 GO:0000010 GO:0000009 GO:0000006 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] |
2023-06-17 04:44:40.411443: FMAVREVLGH: GO:0000010 GO:0000009 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5], [10, 10]] | go:0000007: [[6, 6], [8, 8]] | go:0000003: [[7, 7], [9, 9]] |
2023-06-17 04:44:40.411499: DALIDHWW: GO:0000010 GO:0000009 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6], [8, 8]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:40.411542: QTQYN: GO:0000002 GO:0000009 GO:0000008 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] |
2023-06-17 04:44:40.411565: G: GO:0000010 GO:0000009 GO:0000008 GO:0000001 GO:0000005 GO:0000004: go:0000010: [[1, 1]] |
2023-06-17 04:44:40.411610: IQLHWCAAA: GO:0000010 GO:0000009 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6], [8, 8]] | go:0000003: [[7, 7], [9, 9]] |
2023-06-17 04:44:40.411632: T: GO:0000010 GO:0000001 GO:0000009 GO:0000004: go:0000010: [[1, 1]] |
2023-06-17 04:44:40.411677: TGERVTPM: GO:0000010 GO:0000009 GO:0000008 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:40.411711: MITP: GO:0000002 GO:0000009 GO:0000001 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] |
2023-06-17 04:44:40.411742: LYDF: GO:0000010 GO:0000009 GO:0000001 GO:0000006 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] |
2023-06-17 04:44:40.411786: LDGNCHFL: GO:0000002 GO:0000010 GO:0000009 GO:0000008 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:40.411808: P: GO:0000010 GO:0000009 GO:0000008 GO:0000001 GO:0000005 GO:0000004: go:0000010: [[1, 1]] |
2023-06-17 04:44:40.411842: DAHF: GO:0000002 GO:0000009 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] |
2023-06-17 04:44:40.411881: MCWDIS: GO:0000002 GO:0000010 GO:0000009 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] |
2023-06-17 04:44:40.411911: RGM: GO:0000010 GO:0000009 GO:0000008 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] |
2023-06-17 04:44:40.411950: NNTHNH: GO:0000010 GO:0000009 GO:0000008 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6]] |
2023-06-17 04:44:40.411976: ML: GO:0000010 GO:0000002 GO:0000009 GO:0000008 GO:0000006 GO:0000005 GO:0000003: go:0000010: [[1, 1]] | go:0000009: [[2, 2]] |
2023-06-17 04:44:40.412019: YYCGCHIG: GO:0000002 GO:0000010 GO:0000009 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5], [8, 8]] | go:0000007: [[6, 6]] | go:0000003: [[7, 7]] |
2023-06-17 04:44:40.412055: KVLTI: GO:0000010 GO:0000009 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] |
2023-06-17 04:44:40.412077: R: GO:0000010 GO:0000009 GO:0000001 GO:0000005 GO:0000004: go:0000010: [[1, 1]] |
2023-06-17 04:44:40.412103: VNY: GO:0000002 GO:0000001 GO:0000006 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] |
2023-06-17 04:44:40.412130: LPN: GO:0000008 GO:0000001 GO:0000006 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] |
2023-06-17 04:44:40.412173: TKGPNDNA: GO:0000009 GO:0000001 GO:0000007 GO:0000006 GO:0000005 GO:0000004 GO:0000003: go:0000006: [[1, 1]] | go:0000004: [[2, 2]] | go:0000001: [[3, 3]] | go:0000009: [[4, 4]] | go:0000005: [[5, 5]] | go:0000007: [[6, 6], [8, 8]] | go:0000003: [[7, 7]] |

In the output above, the script first loads and executes the classification head model. Then, the transformer model is loaded and executed. After that, the merged results are listed at the end.

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