Prediction of Drug-Related Diseases Through Integrating Pairwise Attributes and Neighbor Topological Structures

Identifying new disease indications for the approved drugs can help reduce the cost and time of drug development. Most of the recent methods focus on exploiting the various information related to drugs and diseases for predicting the candidate drug-disease associations. However, the previous methods...

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Bibliographic Details
Published in:IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 19; no. 5; pp. 2963 - 2974
Main Authors: Song, Yingying, Cui, Hui, Zhang, Tiangang, Yang, Tingxiao, Li, Xiaokun, Xuan, Ping
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2022
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Bilayers
Case studies
convolutional neural network
Data models
Disease
Diseases
Drug development
Drug-disease association prediction
Drugs
Embedding
Encoding
fully-connected autoencoder
Heterogeneous networks
Information processing
MicroRNAs
Multilayers
neighbor topological structure
Network topology
Neural networks
Neural Networks, Computer
Nodes
pairwise attribute representation
Random walk
Representations
Similarity
Topology
ISSN:1545-5963, 1557-9964, 2374-0043, 1557-9964
Online Access:Get full text
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Summary:Identifying new disease indications for the approved drugs can help reduce the cost and time of drug development. Most of the recent methods focus on exploiting the various information related to drugs and diseases for predicting the candidate drug-disease associations. However, the previous methods failed to deeply integrate the neighborhood topological structure and the node attributes of an interested drug-disease node pair. We propose a new prediction method, ANPred, to learn and integrate pairwise attribute information and neighbor topology information from the similarities and associations related to drugs and diseases. First, a bi-layer heterogeneous network with intra-layer and inter-layer connections is established to combine the drug similarities, the disease similarities, and the drug-disease associations. Second, the embedding of a pair of drug and disease is constructed based on integrating multiple biological premises about drugs and diseases. The learning framework based on multi-layer convolutional neural networks is designed to learn the attribute representation of the pair of drug and disease nodes from its embedding. The sequences composed of neighbor nodes are formed based on random walk on the heterogeneous network. A framework based on fully-connected autoencoder and skip-gram module is constructed to learn the neighbor topological representations of nodes. The cross-validation results indicate the performance of ANPred is superior to several state-of-the-art methods. The case studies on 5 drugs further confirm the ability of ANPred in discovering the potential drug-disease association candidates.
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ISSN:1545-5963
1557-9964
2374-0043
1557-9964
DOI:10.1109/TCBB.2021.3089692