Explainable artificial intelligence model for identifying COVID-19 gene biomarkers. (March 2023)

• Record Type: Journal Article
• Authors: Yagin, Fatma Hilal; Cicek, İpek Balikci; Alkhateeb, Abedalrhman; Yagin, Burak; Colak, Cemil; Azzeh, Mohammad; Akbulut, Sami
• Is Part Of: Computers in biology and medicine. Volume 154(2023)
• Publication Date: 2023-03
• Subjects: COVID-19 -- Explainable artificial intelligence -- LIME -- SHAP -- XGBoost; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106619
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Aim: COVID-19 has revealed the need for fast and reliable methods to assist clinicians in diagnosing the disease. This article presents a model that applies explainable artificial intelligence (XAI) methods based on machine learning techniques on COVID-19 metagenomic next-generation sequencing (mNGS) samples. Methods: In the data set used in the study, there are 15, 979 gene expressions of 234 patients with COVID-19 negative 141 (60.3%) and COVID-19 positive 93 (39.7%). The least absolute shrinkage and selection operator (LASSO) method was applied to select genes associated with COVID-19. Support Vector Machine - Synthetic Minority Oversampling Technique (SVM-SMOTE) method was used to handle the class imbalance problem. Logistics regression (LR), SVM, random forest (RF), and extreme gradient boosting (XGBoost) methods were constructed to predict COVID-19. An explainable approach based on local interpretable model-agnostic explanations (LIME) and SHAPley Additive exPlanations (SHAP) methods was applied to determine COVID-19- associated biomarker candidate genes and improve the final model's interpretability. Results: For the diagnosis of COVID-19, the XGBoost (accuracy: 0.930) model outperformed the RF (accuracy: 0.912), SVM (accuracy: 0.877), and LR (accuracy: 0.912) models. As a result of the SHAP, the three most important genes associated with COVID-19 were IFI27, LGR6, and FAM83A. The results of LIME showed that especially the high level of IFI27 gene expression contributed to increasing the probability of positive class. Conclusions: The proposed model (XGBoost) was able to predict COVID-19 successfully. The results show that machine learning combined with LIME and SHAP can explain the biomarker prediction for COVID-19 and provide clinicians with an intuitive understanding and interpretability of the impact of risk factors in the model. Highlights: Explainable AI approach for COVID-19 diagnosis based on next-generation sequencing. XGBoost is a robust method for detecting COVID-19. LIME provides local explanations of the relative importance of each gene. SHAP provides global explanations for the genes that cause COVID-19. Predictive qualities in the detection of genomic biomarkers of COVID-19 are presented.