Aspect-Level Drug Reviews Sentiment Analysis and COVID-19 Drug prediction using PPI & Deep Learning

Rohit Shivdas Jayale, S. Desai
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引用次数: 1

Abstract

The immense pressure and tension has created in the worldwide healthcare systems by disease. Various existing system has defined drug prediction system based on current patient evaluation. In this research we proposed a drug prediction for COVID-19 patient based on protein to protein reactions and availability. In order to evaluate the protein-protein interactions (PPIs) between some of the virus and individual receptors that are also confirmed utilizing biomedical simulations, the framework also defines machine learning models. The classification techniques are consistent with the predictions of separate physical material sequence-based characteristics such as classification of amino acids, distribution of pseudo amino acids and conjoint triads. Finally we will evaluate the system with numerous machine learning algorithm and show the effectiveness of propose systems.
使用PPI和深度学习的方面级药物评论情感分析和COVID-19药物预测
疾病给全球医疗保健系统带来了巨大的压力和紧张。现有的各种系统都定义了基于当前患者评价的药物预测系统。在本研究中,我们提出了一种基于蛋白间反应和可用性的COVID-19患者药物预测方法。为了评估一些病毒和个体受体之间的蛋白质-蛋白质相互作用(ppi),该框架还定义了机器学习模型,这些相互作用也利用生物医学模拟得到了证实。该分类技术与基于氨基酸分类、伪氨基酸分布和联合三元组等单独物理物质序列特征的预测相一致。最后,我们将使用多种机器学习算法对系统进行评估,并展示所提出系统的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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