Prediction of Covid-19 Multiparametric Biomarkers and Drug Target of Patients for Risk Stratification Using Machine Learning Approach

Abstract

In the situation of Coronavirus disease 2019 (COVID-19), forecasting disease progression and identifying therapeutic drug targets is critical, especially given the nonattendance of a viable approach for treating severe cases. The preparation cohort revealed promising biomarkers, which were then precisely measured and employed to assess prediction accuracy across validation cohorts. This approach holds significant potential in enhancing understanding of severe COVID-19 and aiding the development of effective treatments. However, ultrasound-guided MRI (US-MRI) is an emerging modality that can noninvasively acquire multi-parametric information on COVID-19 and function without the need for contrast agents. This shows that neural network analysis of US-MRI transports exclusive prognosis data and this significantly improved prognosis performance. Consequently, the research proposed a deep neural network model of an Ensemble Multi-Relational Graph Neural Network (EMR-GNN) to determine the optimal model for predicting vascular biomarkers (CRP, IL-6, ferritin). In the nonappearance of a tailored treatment for this emerging virus, scientists are actively investigating various strategies to curb its replication. This work focuses on identifying potential drug targets, drawing from proteins abundant in lung material and those targeted by FDA-approved drugs as catalogued in HPA. This effort reflects a broader initiative within the methodical unrestricted to develop effective means of limiting virus replication. Accordingly, recognized five lung-improved proteins, comprising MRC1, SG3A1, CCL18, histone H4, and CLEC3B, were annotated as “drug targets”. For this, the researcher proposes a Heterogeneous Graph Structural Attention Neural Network (HGS-ANN) model to learn topological information of composite molecules and a Dilated Causal CNN-LSTM model with U-Net layers for modelling spatial-sequential information in Simplified Molecular-Input Line-Entry System (SMILES) sequences of drug data. The COVID-19 datasets are downloaded from the GEO database. These data are evaluated using Matlab software. The proposed work evaluated that the AUC of the work is 0.995, however, the AUC is measured based on sex, age, and chronic diseases. This model has a 0.933 accuracy in the subgroup of slices thicker than 1mm. However, the AUC curve and the classification outcome of the proposed method are compared with the existing rad model, deeper, and KNN models. In comparison to existing methods, the proposed model demonstrates superior performance. This research not only identifies potential therapeutic targets nonetheless also serves to uncover biomarkers crucial for comprehending the pathogenesis of undecorated COVID-19.