Disease-related compound identification based on deeping learning method.
Sci Rep
; 12(1): 20594, 2022 Nov 29.
Article
in English
| MEDLINE | ID: covidwho-2133621
ABSTRACT
Acute lung injury (ALI) is a serious respiratory disease, which can lead to acute respiratory failure or death. It is closely related to the pathogenesis of New Coronavirus pneumonia (COVID-19). Many researches showed that traditional Chinese medicine (TCM) had a good effect on its intervention, and network pharmacology could play a very important role. In order to construct "disease-gene-target-drug" interaction network more accurately, deep learning algorithm is utilized in this paper. Two ALI-related target genes (REAL and SATA3) are considered, and the active and inactive compounds of the two corresponding target genes are collected as training data, respectively. Molecular descriptors and molecular fingerprints are utilized to characterize each compound. Forest graph embedded deep feed forward network (forgeNet) is proposed to train. The experimental results show that forgeNet performs better than support vector machines (SVM), random forest (RF), logical regression (LR), Naive Bayes (NB), XGBoost, LightGBM and gcForest. forgeNet could identify 19 compounds in Erhuang decoction (EhD) and Dexamethasone (DXMS) more accurately.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Respiratory Distress Syndrome
/
Acute Lung Injury
/
COVID-19 Drug Treatment
Type of study:
Prognostic study
/
Randomized controlled trials
Topics:
Traditional medicine
Limits:
Humans
Language:
English
Journal:
Sci Rep
Year:
2022
Document Type:
Article
Affiliation country:
S41598-022-24385-1
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