Large scale PTM - PPI relation extraction

Related tags

Deep Learninglarge-scale-ptm-ppi
Overview

Build Status

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

docs/images/Overview.png

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

AIMed PPI relation extraction

Download AIMed dataset

  1. Download from ftp://ftp.cs.utexas.edu/pub/mooney/bio-data/interactions.tar.gz

  2. 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

  1. 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"
You might also like...
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

ZJUNLP 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

万理 5 Jul 26, 2022
It's a implement of this paper：Relation extraction via Multi-Level attention CNNs
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

Aybss 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.

Jieun Han 1 Oct 6, 2022
A pytorch-version implementation codes of paper:
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

null 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

EleutherAI 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

The Apache Software Foundation 34.7k Jan 4, 2023
This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data.

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

null 212 Dec 25, 2022
[ICLR 2021, Spotlight] Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
[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

Shengyu Zhao 373 Jan 2, 2023
Comments
  • CVE-2007-4559 Patch

    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.

    1. 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")
    1. 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
HybridNets: End-to-End Perception Network

HybridNets: End2End Perception Network HybridNets Network Architecture. HybridNets: End-to-End Perception Network by Dat Vu, Bao Ngo, Hung Phan 📧 FPT

Thanh Dat Vu 370 Dec 29, 2022
An open-source Deep Learning Engine for Healthcare that aims to treat & prevent major diseases

AlphaCare Background AlphaCare is a work-in-progress, open-source Deep Learning Engine for Healthcare that aims to treat and prevent major diseases. T

Siraj Raval 44 Nov 05, 2022
learning and feeling SLAM together with hands-on-experiments

modern-slam-tutorial-python Learning and feeling SLAM together with hands-on-experiments 😀 😃 😆 Dependencies Most of the examples are based on GTSAM

Giseop Kim 59 Dec 22, 2022
pixelNeRF: Neural Radiance Fields from One or Few Images

pixelNeRF: Neural Radiance Fields from One or Few Images Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa UC Berkeley arXiv: http://arxiv.org/abs/2

Alex Yu 1k Jan 04, 2023
Unoffical implementation about Image Super-Resolution via Iterative Refinement by Pytorch

Image Super-Resolution via Iterative Refinement Paper | Project Brief This is a unoffical implementation about Image Super-Resolution via Iterative Re

LiangWei Jiang 2.5k Jan 02, 2023
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

Status: Under development (expect bug fixes and huge updates) ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectiv

37 Dec 28, 2022
Bridging Composite and Real: Towards End-to-end Deep Image Matting

Bridging Composite and Real: Towards End-to-end Deep Image Matting Please note that the official repository of the paper Bridging Composite and Real:

Jizhizi_Li 30 Oct 31, 2022
Structured Edge Detection Toolbox

################################################################### # # # Structure

Piotr Dollar 779 Jan 02, 2023
Temporally Coherent GAN SIGGRAPH project.

TecoGAN This repository contains source code and materials for the TecoGAN project, i.e. code for a TEmporally COherent GAN for video super-resolution

Duc Linh Nguyen 2 Jan 18, 2022
Memory-efficient optimum einsum using opt_einsum planning and PyTorch kernels.

opt-einsum-torch There have been many implementations of Einstein's summation. numpy's numpy.einsum is the least efficient one as it only runs in sing

Haoyan Huo 9 Nov 18, 2022
Match SafeGraph POIs with Data collected through a cultural resource survey in Washington DC.

Match SafeGraph POI data with Cultural Resource Places in Washington DC Match SafeGraph POIs with Data collected through a cultural resource survey in

Changjie Chen 1 Jan 05, 2022
This is a TensorFlow implementation for C2-Rec

This is a TensorFlow implementation for C2-Rec We refer to the repo SASRec. Requirements requirement.txt Datasets This repo includes Amazon Beauty dat

7 Nov 14, 2022
Code for DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents

DeepXML Code for DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents Architectures and algorithms DeepXML supports

Extreme Classification 49 Nov 06, 2022
A PyTorch implementation of the baseline method in Panoptic Narrative Grounding (ICCV 2021 Oral)

A PyTorch implementation of the baseline method in Panoptic Narrative Grounding (ICCV 2021 Oral)

Biomedical Computer Vision @ Uniandes 52 Dec 19, 2022
XViT - Space-time Mixing Attention for Video Transformer

XViT - Space-time Mixing Attention for Video Transformer This is the official implementation of the XViT paper: @inproceedings{bulat2021space, title

Adrian Bulat 33 Dec 23, 2022
[ACL 20] Probing Linguistic Features of Sentence-level Representations in Neural Relation Extraction

REval Table of Contents Introduction Overview Requirements Installation Probing Usage Citation License 🎓 Introduction REval is a simple framework for

13 Jan 06, 2023
Yolact-keras实例分割模型在keras当中的实现

Yolact-keras实例分割模型在keras当中的实现 目录 性能情况 Performance 所需环境 Environment 文件下载 Download 训练步骤 How2train 预测步骤 How2predict 评估步骤 How2eval 参考资料 Reference 性能情况 训练数

Bubbliiiing 11 Dec 26, 2022
Algorithms for outlier, adversarial and drift detection

Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline d

Seldon 1.6k Dec 31, 2022
A Benchmark For Measuring Systematic Generalization of Multi-Hierarchical Reasoning

Orchard Dataset This repository contains the code used for generating the Orchard Dataset, as seen in the Multi-Hierarchical Reasoning in Sequences: S

Bill Pung 1 Jun 05, 2022
Repo for the paper "DiLBERT: Cheap Embeddings for Disease Related Medical NLP"

DiLBERT Repo for the paper "DiLBERT: Cheap Embeddings for Disease Related Medical NLP" Pretrained Model The pretrained model presented in the paper is

Kevin Roitero 2 Dec 15, 2022