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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE/ACM Transactions on Computational Biology and Bioinformatics Ročník 19; číslo 5; s. 2963 - 2974
Hlavní autori:	Song, Yingying, Cui, Hui, Zhang, Tiangang, Yang, Tingxiao, Li, Xiaokun, Xuan, Ping
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.09.2022 Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1545-5963 1557-9964 2374-0043 1557-9964
DOI:	10.1109/TCBB.2021.3089692