A deep graph model for the signed interaction prediction in biological network

• URL: http://arxiv.org/abs/2407.07357v1
• Date: Wed, 10 Jul 2024 04:28:21 GMT
• Title: A deep graph model for the signed interaction prediction in biological network
• Authors: Shuyi Jin, Mengji Zhang, Meijie Wang, Lun Yu,
• Abstract summary: In pharmaceutical research, the strategy of drug repurposing accelerates the development of new therapies while reducing R&D costs. Deep graph models have become essential for their precision in mapping complex biological networks. Our study introduces an advanced graph model that utilizes graph convolutional networks and tensor decomposition to effectively predict signed chemical-gene interactions.
• Score: 1.03121181235382
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In pharmaceutical research, the strategy of drug repurposing accelerates the development of new therapies while reducing R&D costs. Network pharmacology lays the theoretical groundwork for identifying new drug indications, and deep graph models have become essential for their precision in mapping complex biological networks. Our study introduces an advanced graph model that utilizes graph convolutional networks and tensor decomposition to effectively predict signed chemical-gene interactions. This model demonstrates superior predictive performance, especially in handling the polar relations in biological networks. Our research opens new avenues for drug discovery and repurposing, especially in understanding the mechanism of actions of drugs.

Related papers

• Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network [69.16939798838159]
  We propose EmerGNN, a graph neural network (GNN) that can effectively predict interactions for emerging drugs. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.
  arXiv  Detail & Related papers  (2023-11-15T06:34:00Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs [2.4087148947930634]
  We develop a methodology to predict drug side-effects using Graph Neural Networks. We build a dataset from freely accessible and well established data sources. The results show that our method has an improved classification capability, under many parameters and metrics.
  arXiv  Detail & Related papers  (2022-11-30T10:12:41Z)
• Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks [5.156812030122437]
  Current biological networks are noisy, sparse, and incomplete. Experimental identification of such interactions is both time-consuming and expensive. Deep graph neural networks have shown their effectiveness in modeling graph-structured data and achieved good performance in biomedical interaction prediction. Our proposed method enables the graph convolutional networks to dynamically adapt their depths to accommodate an increasing number of interactions.
  arXiv  Detail & Related papers  (2022-11-22T20:44:28Z)
• Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling [10.898133007285638]
  We develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) The proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network.
  arXiv  Detail & Related papers  (2022-09-19T11:18:45Z)
• Graph neural networks for the prediction of molecular structure-property relationships [59.11160990637615]
  Graph neural networks (GNNs) are a novel machine learning method that directly work on the molecular graph. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
  arXiv  Detail & Related papers  (2022-07-25T11:30:44Z)
• Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model [64.29487107585665]
  Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks. In order to further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning.
  arXiv  Detail & Related papers  (2022-07-14T20:03:52Z)
• 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)
• Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer [98.8319016075089]
  We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture. Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively. IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
  arXiv  Detail & Related papers  (2021-10-14T13:28:02Z)
• Interpretable Drug Synergy Prediction with Graph Neural Networks for Human-AI Collaboration in Healthcare [23.151336811933938]
  We propose a deep graph neural network, IDSP, to incorporate the gene-gene as well as gene-drug regulatory relationships in synergic drug combination predictions. IDSP automatically learns weights of edges based on the gene and drug node relations, by a multi-layer perceptron (MLP) and aggregates information in an inductive manner. We test IDWSP on signaling networks formulated by genes from 46 core cancer signaling pathways and drug combinations from NCI ALMANAC drug combination screening data.
  arXiv  Detail & Related papers  (2021-05-14T22:20:29Z)
• Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions [79.45090959869124]
  We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions.
  arXiv  Detail & Related papers  (2020-07-08T17:21:00Z)

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.