Wang et al, 2021, Briefings in Bioinformatics.
- What kind of research(abstract)?
Drug repurposing and discovery using DeepATC.
ATC is the anatomical therapeutic chemical classification system, defined by WHO.
This method used graph convolutional network, inferring biological network and multimode attentive fusion network.
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What is progress from previous research?
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What is the specific topic?
Five drug networks were constructed according to iATC-NRAKEL.
Three types of chemical-chemical interaction were extracted using STITCH.
Two types of chemical-chemical similarity scores were obtained from KEGG using SIMCOMP and SUBCOMP.
Library of Integrated Network-Based Cellular Signatures (LINCS) is used for creating transcriptome data.
Benchmark dataset of Drug-Target Interaction was proposed according to A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.
The drug-drug interaction was extracted from DrugBank and the protein-protein interactions were obtained from the HPRD.
The drug-disease and protein-disease associations were extracted from Comparative Toxicogenomics Database .
The drug-side effect associations were extracted from the SIDER DB.
The structural similarity of drugs was calculated by the Tanimoto coefficient.
This model is constructed by three submodules
- molecular semantic embedding (MSE) module
- heterogeneous-network embedding (HNE) module
- multimodel attentive fusion (MAF) module
DeepATC: [WIP]
- How did the method evaluate its usefulness?
Compared with KNN, RF, and SVM.
-
Are there any discussions?
-
What is the next article you should read?
Wang et al, 2021, Briefings in Bioinformatics.
Drug repurposing and discovery using DeepATC.
ATC is the anatomical therapeutic chemical classification system, defined by WHO.
This method used graph convolutional network, inferring biological network and multimode attentive fusion network.
What is progress from previous research?
What is the specific topic?
Five drug networks were constructed according to iATC-NRAKEL.
Three types of chemical-chemical interaction were extracted using STITCH.
Two types of chemical-chemical similarity scores were obtained from KEGG using SIMCOMP and SUBCOMP.
Library of Integrated Network-Based Cellular Signatures (LINCS) is used for creating transcriptome data.
Benchmark dataset of Drug-Target Interaction was proposed according to A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.
The drug-drug interaction was extracted from DrugBank and the protein-protein interactions were obtained from the HPRD.
The drug-disease and protein-disease associations were extracted from Comparative Toxicogenomics Database .
The drug-side effect associations were extracted from the SIDER DB.
The structural similarity of drugs was calculated by the Tanimoto coefficient.
This model is constructed by three submodules
DeepATC: [WIP]
Compared with KNN, RF, and SVM.
Are there any discussions?
What is the next article you should read?