A Multi-graph Deep Learning Model for Predicting Drug-Disease Associations

Zhao, Bo-Wei; You, Zhu‐Hong; Hu, Lun; Wong, Leon; Ji, Bo-Ya; Zhang, Ping

doi:10.1007/978-3-030-84532-2_52

Cited by 11 publications

(5 citation statements)

References 56 publications

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“…Different from a simple graph, multigraph allows multiple edges between two nodes. In deep learning, multigraph has been applied in different domains, including clustering [48,43,23], medical image processing [42,83,2], traffic flow prediction [44,84], activity recognition [68], recommendation system [69], and cross-domain adaptation [59]. In this paper, we construct the multigraph to enable isolated nodes and reduce the training time in cross-silo federated learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Multigraph Topology Design for Cross-Silo Federated Learning

Nguyen¹,

Do²,

Nguyen³

et al. 2022

Preprint

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Cross-silo federated learning utilizes a few hundred reliable data silos with high-speed access links to jointly train a model. While this approach becomes a popular setting in federated learning, designing a robust topology to reduce the training time is still an open problem. In this paper, we present a new multigraph topology for cross-silo federated learning. We first construct the multigraph using the overlay graph. We then parse this multigraph into different simple graphs with isolated nodes. The existence of isolated nodes allows us to perform model aggregation without waiting for other nodes, hence reducing the training time. We further propose a new distributed learning algorithm to use with our multigraph topology. The intensive experiments on public datasets show that our proposed method significantly reduces the training time compared with recent state-of-theart topologies while ensuring convergence and maintaining the model's accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Multigraph Topology Design for Cross-Silo Federated Learning

Nguyen¹,

Do²,

Nguyen³

et al. 2022

Preprint

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“…With the improvement of computing power and accumulation of big data, AI is revolutionizing the traditional approach to drug research and development, significantly enhancing efficiency and success rates [11,12]. Based on drug-proteindisease interaction heterogeneous network, many deep learning approaches proposed for predicting drug-disease interactions [13][14][15][16]. These studies primarily focus on enhancing the accuracy and robustness of model predictions by enriching the information of network nodes, or leveraging the mechanistic information of drugs within the network to improve the interpretability of the models.…”

Section: Introductionmentioning

confidence: 99%

Meta-path-based drug efficacy prediction model for active natural compound discovery from Traditional Chinese medicine

Li,

Shen,

Cai

et al. 2024

Preprint

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Identifying the active nature compounds remains a challenge for drug discovery, and new algorithms need to be developed to predict active ingredients from complex natural products. Here, we proposed Meta-DEP, a Meta-paths-based Drug Efficacy Prediction based on drug-protein-disease heterogeneity network, where Meta-paths contains all the shortest paths between drug targets and disease-related proteins in the network and drug efficacy is measured by a predictive score according to drug disease network proximity. Experiments show that Meta-DEP performs better than traditional network topology analysis on drug-disease interaction prediction task. Further investigations demonstrate that the key targets identified by Meta-DEP for drug efficacy are consistent with clinical pharmacological evidence. To prove that Meta-DEP can be used to discover active nature compounds, we apply it to predict the relationship between the monomeric components of traditional Chinese medicine included in the TCMSP database and diseases. Results indicate that Meta-DEP can accurately predict most of the drug-disease pairs included in the TCMSP database. In addition, biological experiments are directly used to demonstrate that the natural compound mined by Meta-DEP from traditional Chinese medicine has a protective effect against myocardial ischemia. Overall, the model developed in this study provides new impetus for driving the nature compound into innovative lead molecule. Code and data are available at https://github.com/t9lex/Meta-DEP.

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“…Recently, deep learning approaches have been particularly successful when dealing with biological data with underlying Euclidean structure [6,8,[15][16][17][18][19]. As more and more biological data are discovered, these biological data not only include invariant biological attributes, such as the amino acid sequence of proteins, the base sequence of RNA molecules and the molecular structure of drugs, but also their network structure information should be considered, i.e.…”

Section: Introductionmentioning

confidence: 99%

A geometric deep learning framework for drug repositioning over heterogeneous information networks

Zhao

Hu³

et al. 2022

Briefings in Bioinformatics

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Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of taking into account the non-Euclidean nature of biomedical network data. To overcome this problem, a deep learning framework, namely DDAGDL, is proposed to predict drug-drug associations (DDAs) by using geometric deep learning (GDL) over heterogeneous information network (HIN). Incorporating complex biological information into the topological structure of HIN, DDAGDL effectively learns the smoothed representations of drugs and diseases with an attention mechanism. Experiment results demonstrate the superior performance of DDAGDL on three real-world datasets under 10-fold cross-validation when compared with state-of-the-art DR methods in terms of several evaluation metrics. Our case studies and molecular docking experiments indicate that DDAGDL is a promising DR tool that gains new insights into exploiting the geometric prior knowledge for improved efficacy.

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A Multi-graph Deep Learning Model for Predicting Drug-Disease Associations

Cited by 11 publications

References 56 publications

Multigraph Topology Design for Cross-Silo Federated Learning

Multigraph Topology Design for Cross-Silo Federated Learning

Meta-path-based drug efficacy prediction model for active natural compound discovery from Traditional Chinese medicine

A geometric deep learning framework for drug repositioning over heterogeneous information networks

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