Large scale PTM - PPI relation extraction

Last update: Feb 25, 2022

Related tags

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"

Code and datasets for the paper "KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction"

KnowPrompt Code and datasets for our paper "KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction" Requireme

137 Dec 31, 2022

Wanli Li and Tieyun Qian: Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction, IJCNN 2021

MRefG Wanli Li and Tieyun Qian: "Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction", IJCNN 2021 1. Requirements To reproduc

5 Jul 26, 2022

It's a implement of this paper：Relation extraction via Multi-Level attention CNNs

Relation Classification via Multi-Level Attention CNNs It's a implement of this paper：Relation Classification via Multi-Level Attention CNNs. Training

2 Nov 4, 2022

Company clustering with K-means/GMM and visualization with PCA, t-SNE, using SSAN relation extraction

RE results graph visualization and company clustering Installation pip install -r requirements.txt python -m nltk.downloader stopwords python3.7 main.

1 Oct 6, 2022

A pytorch-version implementation codes of paper: "BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation"

BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation A pytorch-version implementation

11 Oct 8, 2022

Open-AI's DALL-E for large scale training in mesh-tensorflow.

DALL-E in Mesh-Tensorflow [WIP] Open-AI's DALL-E in Mesh-Tensorflow. If this is similarly efficient to GPT-Neo, this repo should be able to train mode

432 Dec 16, 2022

Apache Spark - A unified analytics engine for large-scale data processing

Apache Spark Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an op

34.7k Jan 4, 2023

This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data.

212 Dec 25, 2022

[ICLR 2021, Spotlight] Large Scale Image Completion via Co-Modulated Generative Adversarial Networks

Large Scale Image Completion via Co-Modulated Generative Adversarial Networks, ICLR 2021 (Spotlight) Demo | Paper [NEW!] Time to play with our interac

373 Jan 2, 2023

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 Repository

Neural Message Passing for Computer Vision

Neural Message Passing for Quantum Chemistry Implementation of different models of Neural Networks on graphs as explained in the article proposed by G

310 Nov 07, 2022

WormMovementSimulation - 3D Simulation of Worm Body Movement with Neurons attached to its body

Generate 3D Locomotion Data This module is intended to create 2D video trajector

1 Aug 09, 2022

Sparse Physics-based and Interpretable Neural Networks

Sparse Physics-based and Interpretable Neural Networks for PDEs This repository contains the code and manuscript for research done on Sparse Physics-b

28 Jan 03, 2023

Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness

Orthogonalizing Convolutional Layers with the Cayley Transform This repository contains implementations and source code to reproduce experiments for t

36 Dec 30, 2022

Athena is the only tool that you will ever need to optimize your portfolio.

Athena Portfolio optimization is the process of selecting the best portfolio (asset distribution), out of the set of all portfolios being considered,

1 Mar 25, 2022

Convolutional Neural Network for 3D meshes in PyTorch

MeshCNN in PyTorch SIGGRAPH 2019 [Paper] [Project Page] MeshCNN is a general-purpose deep neural network for 3D triangular meshes, which can be used f

1.4k Jan 04, 2023

Additional environments compatible with OpenAI gym

Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning A codebase for training reinforcement learning policies for quad

40 Dec 06, 2022

Awesome-AI-books - Some awesome AI related books and pdfs for learning and downloading

Awesome AI books Some awesome AI related books and pdfs for downloading and learning. Preface This repo only used for learning, do not use in business

1k Jan 01, 2023

Train DeepLab for Semantic Image Segmentation

Train DeepLab for Semantic Image Segmentation Martin Kersner, [email protected]

172 Dec 14, 2022

Official implementation for "Image Quality Assessment using Contrastive Learning"

Image Quality Assessment using Contrastive Learning Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli and Alan C. Bovik This is the offi

67 Dec 30, 2022

This package implements the algorithms introduced in Smucler, Sapienza, and Rotnitzky (2020) to compute optimal adjustment sets in causal graphical models.

optimaladj: A library for computing optimal adjustment sets in causal graphical models This package implements the algorithms introduced in Smucler, S

6 Aug 04, 2022