Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction
- URL: http://arxiv.org/abs/2006.14002v1
- Date: Thu, 11 Jun 2020 04:49:26 GMT
- Title: Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction
- Authors: Yunsheng Bai, Ken Gu, Yizhou Sun, Wei Wang
- Abstract summary: We introduce Bi-GNN for modeling biological link prediction tasks such as drug-drug interaction (DDI) and protein-protein interaction (PPI)
Our model not only allows the usage of information from both the high-level interaction graph and the low-level representation graphs, but also offers a baseline for future research opportunities to address the bi-level nature of the data.
- Score: 33.68442018194687
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce Bi-GNN for modeling biological link prediction tasks such as
drug-drug interaction (DDI) and protein-protein interaction (PPI). Taking
drug-drug interaction as an example, existing methods using machine learning
either only utilize the link structure between drugs without using the graph
representation of each drug molecule, or only leverage the individual drug
compound structures without using graph structure for the higher-level DDI
graph. The key idea of our method is to fundamentally view the data as a
bi-level graph, where the highest level graph represents the interaction
between biological entities (interaction graph), and each biological entity
itself is further expanded to its intrinsic graph representation
(representation graphs), where the graph is either flat like a drug compound or
hierarchical like a protein with amino acid level graph, secondary structure,
tertiary structure, etc. Our model not only allows the usage of information
from both the high-level interaction graph and the low-level representation
graphs, but also offers a baseline for future research opportunities to address
the bi-level nature of the data.
Related papers
- Hypergraph-enhanced Dual Semi-supervised Graph Classification [14.339207883093204]
We propose a Hypergraph-Enhanced DuAL framework named HEAL for semi-supervised graph classification.
To better explore the higher-order relationships among nodes, we design a hypergraph structure learning to adaptively learn complex node dependencies.
Based on the learned hypergraph, we introduce a line graph to capture the interaction between hyperedges.
arXiv Detail & Related papers (2024-05-08T02:44:13Z) - H2G2-Net: A Hierarchical Heterogeneous Graph Generative Network Framework for Discovery of Multi-Modal Physiological Responses [3.7110156663640574]
We propose a hierarchical heterogeneous graph generative network (H2G2-Net) that automatically learns a graph structure without domain knowledge.
We validate the proposed method on the CogPilot dataset that consists of multi-modal physiological signals.
arXiv Detail & Related papers (2024-01-05T17:05:33Z) - GraphCL-DTA: a graph contrastive learning with molecular semantics for
drug-target binding affinity prediction [2.523552067304274]
GraphCL-DTA is a graph contrastive learning framework for molecular graphs to learn drug representations.
Next, we design a new loss function that can be directly used to adjust the uniformity of drug and target representations.
The effectiveness of the above innovative elements is verified on two real datasets.
arXiv Detail & Related papers (2023-07-18T06:01:37Z) - Heterogeneous Graph Neural Networks using Self-supervised Reciprocally
Contrastive Learning [102.9138736545956]
Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs.
We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies.
In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
arXiv Detail & Related papers (2022-04-30T12:57:02Z) - Graph-in-Graph (GiG): Learning interpretable latent graphs in
non-Euclidean domain for biological and healthcare applications [52.65389473899139]
Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain.
Recent works have shown that considering relationships between input data samples have a positive regularizing effect for the downstream task.
We propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications.
arXiv Detail & Related papers (2022-04-01T10:01:37Z) - Hierarchical Graph Representation Learning for the Prediction of
Drug-Target Binding Affinity [7.023929372010717]
We propose a novel hierarchical graph representation learning model for the drug-target binding affinity prediction, namely HGRL-DTA.
In this paper, we adopt a message broadcasting mechanism to integrate the hierarchical representations learned from the global-level affinity graph and the local-level molecular graph. Besides, we design a similarity-based embedding map to solve the cold start problem of inferring representations for unseen drugs and targets.
arXiv Detail & Related papers (2022-03-22T04:50:16Z) - CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical
Graph Representation Learning [74.90535111881358]
We propose a new interpretable graph pooling framework - CommPOOL.
It can capture and preserve the hierarchical community structure of graphs in the graph representation learning process.
CommPOOL is a general and flexible framework for hierarchical graph representation learning.
arXiv Detail & Related papers (2020-12-10T21:14:18Z) - Graph Information Bottleneck [77.21967740646784]
Graph Neural Networks (GNNs) provide an expressive way to fuse information from network structure and node features.
Inheriting from the general Information Bottleneck (IB), GIB aims to learn the minimal sufficient representation for a given task.
We show that our proposed models are more robust than state-of-the-art graph defense models.
arXiv Detail & Related papers (2020-10-24T07:13:00Z) - Multilevel Graph Matching Networks for Deep Graph Similarity Learning [79.3213351477689]
We propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects.
To compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks.
Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks.
arXiv Detail & Related papers (2020-07-08T19:48:19Z) - GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity
Interactions [70.9481395807354]
We propose a Graph of Graphs Neural Network (GoGNN), which extracts the features in both structured entity graphs and the entity interaction graph in a hierarchical way.
GoGNN outperforms the state-of-the-art methods on two representative structured entity interaction prediction tasks.
arXiv Detail & Related papers (2020-05-12T03:46:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.