Readings for "A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Therapy, and Drug-Drug Interaction Prediction."

Last update: Jan 05, 2023

Overview

Polypharmacy - DDI - Synergy Survey

The Survey Paper

This repository accompanies our survey paper A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Therapy, and Drug-Drug Interaction Prediction.

If you find the survey or this repository useful in your research, please consider citing our paper:

@inproceedings{poliddicombisurvey,
       title = {{A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Therapy, and Drug-Drug Interaction Prediction}},
       author = {Benedek Rozemberczki and Stephen Bonner and Andriy Nikolov and Michaël Ughetto and Sebastian Nilsson and Eliseo Papa},
       year = {2021}
}

License

Apache 2.0

Comments

✨ Add "Relational Gaifman Models" + dataset
Add "Efficient Learning of Relational Gaifman Models using Probabilistic Logic" and dataset links

It might make sense to refer to one of the papers with respect to "high-level" or "low-level" models (see related publications here: https://srlearn.github.io/relational-datasets/dataset_descriptions/drug_interactions/). For now, this pull request adds links to the 2019 PLP paper under "Datasets."
opened by hayesall 0

Releases(v_00002)

v_00002(Jan 3, 2022)
What's Changed

✨ Add "Relational Gaifman Models" + dataset by @hayesall in https://github.com/AstraZeneca/awesome-drug-pair-scoring/pull/1

New Contributors

@hayesall made their first contribution in https://github.com/AstraZeneca/awesome-drug-pair-scoring/pull/1

Full Changelog: https://github.com/AstraZeneca/awesome-drug-pair-scoring/compare/v_00001...v_00002
Source code(tar.gz)
Source code(zip)
v_00001(Nov 29, 2021)
The repository covers deep learning papers that solve these tasks:

Drug - Drug Interaction Prediction

Polypharmacy Side Effect Prediction

Drug Combination Synergy Scoring

Source code(tar.gz)
Source code(zip)

Owner

AstraZeneca

Data and AI: Unlocking new science insights

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Readings for "A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Therapy, and Drug-Drug Interaction Prediction."

Related tags

Overview

Polypharmacy - DDI - Synergy Survey

The Survey Paper

Contents

You might also like...

Comments

✨ Add "Relational Gaifman Models" + dataset

Releases(v_00002)

v_00002(Jan 3, 2022)

What's Changed

New Contributors

v_00001(Nov 29, 2021)

Owner

AstraZeneca

An unofficial PyTorch implementation of a federated learning algorithm, FedAvg.

Pytorch implementation of the popular Improv RNN model originally proposed by the Magenta team.

Official git repo for the CHIRP project

Real-Time Semantic Segmentation in Mobile device

Reproducing code of hair style replacement method from Barbershorp.

A modular application for performing anomaly detection in networks

This is the official source code for SLATE. We provide the code for the model, the training code, and a dataset loader for the 3D Shapes dataset. This code is implemented in Pytorch.

4K videos with annotated masks in our ICCV2021 paper 'Internal Video Inpainting by Implicit Long-range Propagation'.

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

Random-Afg - Afghanistan Random Old Idz Cloner Tools

A CROSS-MODAL FUSION NETWORK BASED ON SELF-ATTENTION AND RESIDUAL STRUCTURE FOR MULTIMODAL EMOTION RECOGNITION

The official re-implementation of the Neurips 2021 paper, "Targeted Neural Dynamical Modeling".

Implementation of Hire-MLP: Vision MLP via Hierarchical Rearrangement and An Image Patch is a Wave: Phase-Aware Vision MLP.

Code for the paper: Hierarchical Reinforcement Learning With Timed Subgoals, published at NeurIPS 2021

For AILAB: Cross Lingual Retrieval on Yelp Search Engine

Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis

Python Auto-ML Package for Tabular Datasets

A torch implementation of "Pixel-Level Domain Transfer"

Earthquake detection via fiber optic cables using deep learning

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