Open AccessGenerative Adversarial Networks in Disease Gene Drug Relationships
Author(s) -
S. Vijaya
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1055/1/012120
Subject(s) - generative grammar , ambiguity , domain (mathematical analysis) , artificial intelligence , task (project management) , computer science , machine learning , deep learning , disease , drug discovery , gene , drug repositioning , adversarial system , drug , computational biology , bioinformatics , biology , medicine , engineering , genetics , mathematics , pharmacology , mathematical analysis , systems engineering , pathology , programming language
The swift growth in the form of digital information stored in the biomedical databases in this digital era has activated a prototype shift in the models in the Deep learning approaches which have used in several contests in Machine Learning and in the domain of pattern recognition. Finding relationship between entities like genes, diseases, proteins and drugs is tedious task due the ambiguity in the terms used in biomedical domain. Treating cancer with the drug based on the gene that is associated with the disease increases the survival rate. Hence, the deep learning method “Generative Adversarial Networks” is proposed to find the relationship between Genes, Diseases and Drugs from biomedical abstracts in this work.