Explainable artificial intelligence model for identifying COVID-19 gene biomarkers

Yağın, Fatma Hilal; ÇİÇEK, İpek BALIKÇI; Alkhateeb, Abedalrhman; Yagin, Burak; Çolak, Cemil; Azzeh, Muhammed; Akbulut, Sami

doi:10.1016/j.compbiomed.2023.106619

Cited by 29 publications

(12 citation statements)

References 43 publications

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“…This study further highlights the potential role of variants with similar MAFs for the use of biomarkers for COVID-19. Yagin et al (2023) recently developed artificial intelligence model (extreme gradient boosting) gene expression profiles, capable of successfully predicting COVID-19 [176]. Similarly, SNPs should be studied for their potential use as predictive biomarkers for COVID-19 severity.…”

Section: Sars-cov-2 Receptor-based Therapy and Clinical Significancementioning

confidence: 99%

Genetic Variants within SARS-CoV-2 Human Receptor Genes May Contribute to Variable Disease Outcomes in Different Ethnicities

Adimulam

Arumugam

Gokul

et al. 2023

IJMS

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The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved into a global pandemic, with an alarming infectivity and mortality rate. Studies have examined genetic effects on SARS-CoV-2 disease susceptibility and severity within Eurasian populations. These studies identified contrasting effects on the severity of disease between African populations. Genetic factors can explain some of the diversity observed within SARS-CoV-2 disease susceptibility and severity. Single nucleotide polymorphisms (SNPs) within the SARS-CoV-2 receptor genes have demonstrated detrimental and protective effects across ethnic groups. For example, the TT genotype of rs2285666 (Angiotensin-converting enzyme 2 (ACE2)) is associated with the severity of SARS-CoV-2 disease, which is found at higher frequency within Asian individuals compared to African and European individuals. In this study, we examined four SARS-CoV-2 receptors, ACE2, Transmembrane serine protease 2 (TMPRSS2), Neuropilin-1 (NRP1), and Basigin (CD147). A total of 42 SNPs located within the four receptors were reviewed: ACE2 (12), TMPRSS2 (10), BSG (CD147) (5), and NRP1 (15). These SNPs may be determining factors for the decreased disease severity observed within African individuals. Furthermore, we highlight the absence of genetic studies within the African population and emphasize the importance of further research. This review provides a comprehensive summary of specific variants within the SARS-CoV-2 receptor genes, which can offer a better understanding of the pathology of the SARS-CoV-2 pandemic and identify novel potential therapeutic targets.

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Section: Sars-cov-2 Receptor-based Therapy and Clinical Significancementioning

confidence: 99%

Genetic Variants within SARS-CoV-2 Human Receptor Genes May Contribute to Variable Disease Outcomes in Different Ethnicities

Adimulam

Arumugam

Gokul

et al. 2023

IJMS

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“…In tenfold CV, the dataset is split into ten equal parts, with each part used once for evaluation and the rest for training, repeated ten times. This procedure, repeated 5 times with new random partitions, offers a robust assessment of the model's performance and reduces variability in performance estimates 23 – 25 .…”

Section: Methodsmentioning

confidence: 99%

A proposed tree-based explainable artificial intelligence approach for the prediction of angina pectoris

Guldogan,

Yagin,

Pinar

et al. 2023

Sci Rep

Self Cite

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Cardiovascular diseases (CVDs) are a serious public health issue that affects and is responsible for numerous fatalities and impairments. Ischemic heart disease (IHD) is one of the most prevalent and deadliest types of CVDs and is responsible for 45% of all CVD-related fatalities. IHD occurs when the blood supply to the heart is reduced due to narrowed or blocked arteries, which causes angina pectoris (AP) chest pain. AP is a common symptom of IHD and can indicate a higher risk of heart attack or sudden cardiac death. Therefore, it is important to diagnose and treat AP promptly and effectively. To forecast AP in women, we constructed a novel artificial intelligence (AI) method employing the tree-based algorithm known as an Explainable Boosting Machine (EBM). EBM is a machine learning (ML) technique that combines the interpretability of linear models with the flexibility and accuracy of gradient boosting. We applied EBM to a dataset of 200 female patients, 100 with AP and 100 without AP, and extracted the most relevant features for AP prediction. We then evaluated the performance of EBM against other AI methods, such as Logistic Regression (LR), Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM). We found that EBM was the most accurate and well-balanced technique for forecasting AP, with accuracy (0.925) and Youden's index (0.960). We also looked at the global and local explanations provided by EBM to better understand how each feature affected the prediction and how each patient was classified. Our research showed that EBM is a useful AI method for predicting AP in women and identifying the risk factors related to it. This can help clinicians to provide personalized and evidence-based care for female patients with AP.

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“…Shapley Additive Explanations (SHAP) is a novel game theory-based approach in explainable ML introduced by Lundberg and Lee [ 10 ], it can well solve the issue of inexplicability by providing a solution for better understanding and interpreting complex models, and this method allows for representing the contribution of each feature to the outcome. Yagin et al proposed an explainable artificial intelligence model to predict COVID-19 using metagenomic next-generation sequencing (mNGS) data, and the model allowed physicians to enhance their comprehension of the decision-making process in COVID-19 genomic prediction [ 11 ]. Another study developed a XGBoost model combined with SHAP to effectively predict the 3-year all-cause mortality in coronary heart disease and heart failure patients.…”

Section: Introductionmentioning

confidence: 99%

Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage

Shu,

Yan,

et al. 2024

Aging

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Objective: Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP). Methods: A prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH. The optimal model was selected for SHAP analysis. Results: Among the 421 patients, 204 (48.5%) exhibited poor prognosis. The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0 .929). Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis. Additionally, the SHAP force plot visualized individual prognosis predictions. Conclusions: This study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. The features identified through SHAP analysis enhance model interpretability and provide guidance for clinical decision-making.

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Explainable artificial intelligence model for identifying COVID-19 gene biomarkers

Cited by 29 publications

References 43 publications

Genetic Variants within SARS-CoV-2 Human Receptor Genes May Contribute to Variable Disease Outcomes in Different Ethnicities

Genetic Variants within SARS-CoV-2 Human Receptor Genes May Contribute to Variable Disease Outcomes in Different Ethnicities

A proposed tree-based explainable artificial intelligence approach for the prediction of angina pectoris

Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage

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