Systems Medicine Design based on Systems Biology Approaches and Deep Neural Network for Gastric Cancer

Gastric cancer (GC) is the third leading cause of cancer death in the world. It is associated with the stimulation of microenvironment, aberrant epigenetic modification, and chronic inflammation. However, few researches discuss the GC molecular progression mechanisms from the perspective of the syst...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2022-09, Vol.19 (5), p.3019-3031
Main Authors: Yeh, Shan-Ju, Chen, Bor-Sen
Format: Article
Language:English
Subjects:
Artificial neural networks
Biology and genetics
Biomarkers
Cancer
Drug development
Drugs
Epigenetics
Gastric cancer
Gene regulation
Genomes
Machine learning
Medicine
Microenvironments
Modeling techniques
Molecular biology
Neural networks
Next-generation sequencing
Protein interaction
Proteins
Regulation
System identification
Systems biology
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Summary:Gastric cancer (GC) is the third leading cause of cancer death in the world. It is associated with the stimulation of microenvironment, aberrant epigenetic modification, and chronic inflammation. However, few researches discuss the GC molecular progression mechanisms from the perspective of the system level. In this study, we proposed a systems medicine design procedure to identify essential biomarkers and find corresponding drugs for GC. At first, we did big database mining to construct candidate protein-protein interaction network (PPIN) and candidate gene regulation network (GRN). Second, by leveraging the next-generation sequencing (NGS) data, we performed system modeling and applied system identification and model selection to obtain real genome-wide genetic and epigenetic networks (GWGENs). To make the real GWGENs easy to analyze, the principal network projection method was used to extract the core signaling pathways denoted by KEGG pathways. Subsequently, based on the identified biomarkers, we trained a deep neural network of drug-target interaction (DeepDTI) with supervised learning and filtered our candidate drugs considering drug regulation ability and drug sensitivity. With the proposed systematic strategy, we not only shed the light on the progression of GC but also suggested potential multiple-molecule drugs efficiently.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2021.3095369