Large scale PTM - PPI relation extraction

Last update: Feb 25, 2022

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Overview

Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT

The silver standard data is available https://github.com/elangovana/large-scale-ptm-ppi/releases/download/v1.0.0/distant_silver_data.zip

PTM-PPI Dataset relation extraction

For data preparation, see https://github.com/elangovana/PPI-typed-relation-extractor
For training see, notebooks/ppi_multiclass_sagemaker_bert.ipynb
For large scale prediction, see notebooks/ppi_multiclass_large_scale_prediction.ipynb

AIMed PPI relation extraction

Download AIMed dataset

Download from ftp://ftp.cs.utexas.edu/pub/mooney/bio-data/interactions.tar.gz
Convert the raw dataset into XML for using instructions in http://mars.cs.utu.fi/PPICorpora/
```
convert_aimed.py -i  aimed_interactions_input_dir -o aimed.xml
```

Run

Step 1: Convert xml AIMed to flattened json

python src/preprocessors/aimed_json_converter.py --inputfile tests/sample_data/aimed.xml --outputfile aimed.json

Step 2: 10 fold split

You can either choose random split, or split by unique documents

[Option R - Random ] This randomly splits into n folds

python src/preprocessors/kfold_aimed_json_splitter.py --inputfile aimed.json --outputdir temp_data/kfolds_random  --kfoldLabelColumn interacts --k 10

[Option U - Unique Document] This splits into n folds, taking into account document id uniqueness

python src/preprocessors/kfold_aimed_json_splitter.py --inputfile aimed.json --outputdir temp_data/kfolds_unique  --kfoldLabelColumn interacts --k 10  --kfoldDocId documentId

Step 3: Run training

python src/main_train.py --datasetfactory datasets.aimed_dataset_factory.AimedDatasetFactory --traindir temp_data/kfold_unique --modeldir temp_data --outdir temp_data --kfoldtrainprefix train  --model_config '{"vocab_size": 20000, "hidden_size": 10, "num_hidden_layers": 1, "num_attention_heads": 1, "num_labels": 2}' --tokenisor_data_dir tests/sample_data/tokensior_data --epochs 1 --numworkers 1

Utils

To create the preprocessed file with protein names replaced with markers

python utils/static_markers_ppi_multiclass.py --inputfile /Users/aeg/PycharmProjects/ppi-aimed/temp_data/data/laregscale_hq_above_threshold.json  --outputfile markers_largescale_multiclass.json  --additionalcols "class,confidence"

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Comments

CVE-2007-4559 Patch

Patching CVE-2007-4559

Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

opened by TrellixVulnTeam 0

Releases(v1.0.0)

v1.0.0(Dec 13, 2021)
This is release is the code used for published paper. The silver standard dataset using distant supervision is attached.

The raw files contain the data that require to be processed with markers. Read the json file using python

import pandas as pd pd.read_json("./raw/train_multiclass.json")

The processed files contain the data that are processed with markers. Read the json file using python

import pandas as pd pd.read_json("./processed/train_multiclass.json", orient="records")
Source code(tar.gz)
Source code(zip)
distant_silver_data.zip(932.68 KB)

Owner

GitHub

A graph neural network (GNN) model to predict protein-protein interactions (PPI) with no sample features

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