Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning

• URL: http://arxiv.org/abs/2407.16715v2
• Date: Sat, 27 Jul 2024 15:09:51 GMT
• Title: Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning
• Authors: Yufeng Li, Wenchao Zhao, Bo Dang, Xu Yan, Weimin Wang, Min Gao, Mingxuan Xiao,
• Abstract summary: This paper develops an adverse drug reaction prediction model based on knowledge graph embedding and deep learning. The obtained prediction model has good prediction accuracy and stability, and can provide an effective reference for later safe medication guidance.
• Score: 36.703773706187256
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In clinical treatment, identifying potential adverse reactions of drugs can help assist doctors in making medication decisions. In response to the problems in previous studies that features are high-dimensional and sparse, independent prediction models need to be constructed for each adverse reaction of drugs, and the prediction accuracy is low, this paper develops an adverse drug reaction prediction model based on knowledge graph embedding and deep learning, which can predict experimental results. Unified prediction of adverse drug reactions covered. Knowledge graph embedding technology can fuse the associated information between drugs and alleviate the shortcomings of high-dimensional sparsity in feature matrices, and the efficient training capabilities of deep learning can improve the prediction accuracy of the model. This article builds an adverse drug reaction knowledge graph based on drug feature data; by analyzing the embedding effect of the knowledge graph under different embedding strategies, the best embedding strategy is selected to obtain sample vectors; and then a convolutional neural network model is constructed to predict adverse reactions. The results show that under the DistMult embedding model and 400-dimensional embedding strategy, the convolutional neural network model has the best prediction effect; the average accuracy, F_1 score, recall rate and area under the curve of repeated experiments are better than the methods reported in the literature. The obtained prediction model has good prediction accuracy and stability, and can provide an effective reference for later safe medication guidance.

Related papers

• DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Network [9.641021461914551]
  We propose a novel interpretable predictive model, DRExplainer, for drug response prediction. DRExplainer constructs a directed bipartite network integrating multi-omics profiles of cell lines, the chemical structure of drugs and known drug response. In computational experiments, DRExplainer outperforms state-of-the-art predictive methods and another graph-based explanation method.
  arXiv  Detail & Related papers  (2024-08-22T05:45:48Z)
• 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)
• 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)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings [5.1168938454615205]
  We show that using knowledge graph embeddings can increase the accuracy of effect prediction with neural networks. We evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.
  arXiv  Detail & Related papers  (2021-12-08T22:19:16Z)
• HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data [56.53715632642495]
  Clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment. In this paper, we propose Hierarchical INteraction Network (HINT) for more general, clinical trial outcome predictions.
  arXiv  Detail & Related papers  (2021-02-08T15:09:07Z)
• Patient-independent Epileptic Seizure Prediction using Deep Learning Models [39.19336481493405]
  The purpose of a seizure prediction system is to successfully identify the pre-ictal brain stage, which occurs before a seizure event. Patient-independent seizure prediction models are designed to offer accurate performance across multiple subjects within a dataset. We propose two patient-independent deep learning architectures with different learning strategies that can learn a global function utilizing data from multiple subjects.
  arXiv  Detail & Related papers  (2020-11-18T23:13:48Z)
• Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response [49.86828302591469]
  In this paper, we apply transfer learning to the prediction of anti-cancer drug response. We apply the classic transfer learning framework that trains a prediction model on the source dataset and refines it on the target dataset. The ensemble transfer learning pipeline is implemented using LightGBM and two deep neural network (DNN) models with different architectures.
  arXiv  Detail & Related papers  (2020-05-13T20:29:48Z)

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.