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Öğe A comprehensive analysis and performance evaluation for osteoporosis prediction models(PeerJ Inc., 2024) Shams Alden, Zahraa Noor Aldeen M.; Ata, OğuzMedical data analysis is an expanding area of study that holds the promise of transforming the healthcare landscape. The use of available data by researchers gives guidelines to improve health practitioners’ decision-making capacity, thus enhancing patients’ lives. The study looks at using deep learning techniques to predict the onset of osteoporosis from the NHANES 2017–2020 dataset that was preprocessed and arranged into SpineOsteo and FemurOsteo datasets. Two feature selection methods, namely mutual information (MI) and recursive feature elimination (RFE), were applied to sequential deep neural network models, convolutional neural network models, and recurrent neural network models. It can be concluded from the models that the mutual information method achieved higher accuracy than recursive feature elimination, and the MI feature selection CNN model showed better performance by showing 99.15% accuracy for the SpineOsteo dataset and 99.94% classification accuracy for the FemurOsteo dataset. Key findings of this study include family medical history, cases of fractures in patients and parental hip fractures, and regular use of medications like prednisone or cortisone. The research underscores the potential for deep learning in medical data processing, which eventually opens the way for enhanced models for diagnosis and prognosis based on non-image medical data. The implications of the study shall then be important for healthcare providers to be more informed in their decision-making processes for patients’ outcomes.Öğe An updated analysis of variations in SARS-CoV-2 genome(Tubitak Scientific & Technical Research Council Turkey, 2020) Uğurel, Osman Mutluhan; Ata, Oğuz; Turgut-Balık, DilekA novel pathogen, named SARS-CoV-2, has caused an unprecedented worldwide pandemic in the first half of 2020. As the SARS-CoV-2 genome sequences have become available, one of the important focus of scientists has become tracking variations in the viral genome. In this study, 30366 SARS-CoV-2 isolate genomes were aligned using the software developed by our group (ODOTool) and 11 variations in SARS-CoV-2 genome over 10% of whole isolates were discussed. Results indicated that, frequency rates of these 11 variations change between 3.56%-88.44 % and these rates differ greatly depending on the continents they have been reported. Despite some variations being in low frequency rate in some continents, C14408T and A23403G variations on Nsp12 and S protein, respectively, observed to be the most prominent variations all over the world, in general, and both cause missense mutations. It is also notable that most of isolates carry C14408T and A23403 variations simultaneously and also nearly all isolates carrying the G25563T variation on ORF3a, also carry C14408T and A23403 variations, although their location distributions are not similar. All these data should be considered towards development of vaccine and antiviral treatment strategies as well as tracing diversity of virus in all over the world.Öğe Application area of classification techniques in medicine(Altınbaş Üniversitesi, 2018) Ata, Oğuz; Fayez, MustafaThe health care industry produces a huge amount of data that collects complex patient and medical information. Data mining is popular in various fields of research because of its applications and methodologies to extract information correctly. Data mining techniques have the capabilities to find out veiled forms or relationships among the objects in the medical data. In addition the most data mining algorithms that had used in medical industry until this time are neural network including deep learning, SVM, Bayesian and fizzy logic. The main reason of use these algorithms that because they are gave best results with high accuracy with different type of medicine datasets. Finally, data mining continues with medicine industry to help people with or solve different clinical problems.Öğe Car-like robot path planning based on voronoi and Q-learning algorithms(ICEET, 2021) Alhassow, Mustafa Mohammed; Ata, Oğuz; Atilla, Doğu ÇağdaşThis paper discusses a differential path planning issue for the mobile robot depending on Voronoi diagram (VD) and Q-learning algorithms (QL). The issues with re-arranging paths in a dynamic environment with obstructions are treated as an issue of looking for the best route between the start and target stage. Since the car-like robot is differentially system and its mathematically involved with the inward state of impediment because of that some modification will be embedded like the orientation method. This is a unique instance of a solitary vehicle, which just goes ahead at a consistent speed and can just turn left also, right. Voronoi diagram presents the world encompassing brilliant specialists for robots, computer games, and military issues, so improving the dependability, of arranging the environment using it will help and decrease Q learning calculations by re-updating the Q-table, according to the new state, also decreasing both existence intricacy, relies upon earlier information that came from the environment. The work arrangement was tested for a 2D environment. The result of the proposed work showed better performance in time, speed, and length of the path also it can be utilized as an alternate style of guides. A comparison with other related works is performed and the result of these comparisons showed that our work provides a good trajectory with performance.Öğe Classification of melanonychia, Beau's lines, and nail clubbing based on nail images and transfer learning techniques(2023) Soğukkuyu, Derya Yeliz Coşar; Ata, OğuzBackground: Nail diseases are malformations that appear on the nail plate and are classified according to their own signs and symptoms that may be related to other medical conditions. Although most nail diseases have distinct symptoms, making a differential diagnosis of nail problems can be challenging for medical experts.Method: One early diagnosis method for any dermatological disease is designing an image analysis system based on artificial intelligence (AI) techniques. This article implemented a novel model using a publicly available nail disease dataset to determine the occurrence of three common types of nail diseases. Two classification models based on transfer learning using visual geometry group (VGGNet) were utilized to detect and classify nail diseases from images.Result and Finding: The experimental design results showed good accuracy: VGG16 had a score of 94% accuracy and VGG19 had a 93% accuracy rate. These findings suggest that computer-aided diagnostic systems based on transfer learning can be used to identify multiple-lesion nail diseases.Öğe Comparative analysis of different distributions dataset by using data mining techniques on credit card fraud detection(Univ Osijek, Tech Fac, 2020) Ata, Oğuz; Hazim, LaythBanks suffer multimillion-dollars losses each year for several reasons, the most important of which is due to credit card fraud. The issue is how to cope with the challenges we face with this kind of fraud. Skewed "class imbalance" is a very important challenge that faces this kind of fraud. Therefore, in this study, we explore four data mining techniques, namely naive Bayesian (NB),Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF), on actual credit card transactions from European cardholders. This paper offers four major contributions. First, we used under-sampling to balance the dataset because of the high imbalance class, implying skewed distribution. Second, we applied NB, SVM, KNN, and RF to under-sampled class to classify the transactions into fraudulent and genuine followed by testing the performance measures using a confusion matrix and comparing them. Third, we adopted cross-validation (CV) with 10 folds to test the accuracy of the four models with a standard deviation followed by comparing the results for all our models. Next, we examined these models against the entire dataset (skewed) using the confusion matrix and AUC (Area Under the ROC Curve) ranking measure to conclude the final results to determine which would be the best model for us to use with a particular type of fraud. The results showing the best accuracy for the NB, SVM, KNN and RF classifiers are 97,80%; 97,46%; 98,16% and 98,23%, respectively. The comparative results have been done by using four-division datasets (75:25), (90:10), (66:34) and (80:20) displayed that the RF performs better than NB, SVM, and KNN, and the results when utilizing our proposed models on the entire dataset (skewed), achieved preferable outcomes to the under-sampled dataset.Öğe Davranışsal biyometrinin 5 yılı: Kimlik doğrulama ve anomali tespit uygulamaları(2018) Gümüş, Fatma; Ata, Oğuz; Balık, Hasan HüseyinMevcut biyometrik kullanıcı doğrulama teknikleri ikiye ayrılabilir: fizyolojik ve davranışsal yaklaşımlar. Fizyolojik biyometri, bir kişinin parmak izi, yüz, iris/retina ve el/avuç içi gibi fiziksel özellikleri ile ilişkili iken davranış biyometrisi, bir kişinin ses, yazılı imzalama, yürüyüş, yazım ritmi (tuş vuruş dinamikleri) ve dokunmatik dinamikleri gibi davranış modeliyle ilgilidir. Bu çalışmada son beş yılda en yaygın olarak incelenen davranışsal biyometri yöntemlerinin kimlik doğrulama ve anomali tespit uygulamalarında kullanılan öznitelikler ve çalışmaların performansları incelenmiştir. Davranışsal biyometri yöntemleri üç ana başlıkta incelenmiştir: Vücut dinamiklerine dayalı davranışsal biyometri (yürüyüş ve üst vücut dinamikleri, ses ve konuşma, göz hareketleri ve bakış, dudak hareketleri), bilgisayar çevre bileşenleri ve taşınabilir cihaz etkileşimine dayalı davranışsal biyometri (tuş vuruş dinamikleri, fare etkileşimi, dokunmatik ekran etkileşimi, diğer taşınabilir cihaz etkileşimi), imza ve davranış dinamikleriÖğe Diabetic retinopathy detection using developed hybrid cascaded multi-scale DCNN with hybrid heuristic strategy(2024) Tabtaba, Ahlam Asadig Ali; Ata, OğuzIn recent times, Diabetic Retinopathy (DR) is the most inevitable ailment caused by high blood sugar levels in humans. Conversely, the detection process is accomplished by many learning algorithms. Some models have used the single view of retinal images, it renders unsatisfactory outcomes to forecast the disorder. Due to inadequate of retinal lesion features, the system gradually degrades the performance, and the system has a fewer tendencies to diagnose the disease. On the other hand, the computer-based model is suggested to detect the DR. Nevertheless, these methods are most cost and computationally effective and behinds with feature representation, futile to classify the diseases. To conquer against these shortcomings, a novel DR diagnosis model is proposed using a hybrid heuristic-aided deep learning model with fundus images. The retinal fundus images are collected that are given to the pre-processing stages as scaling, cropping and "Contrast Limited Adaptive Histogram Equalization (CLAHE)". In image augmentation, the high quality image is given for further processing with the help of Generative Adversarial Networks (GANs). Subsequently, the augmented images are fed into the model of a Hybrid cascaded Multi-scale Dilated Convolutional Neural Network (HCMD-CNN), where the Residual Attention Network (RAN) and the MobileNet are integrated to provide promising results for DR detection. Furthermore, the parameters present inside the HCMD-CNN are optimized to achieve higher performance with the help of newly designed Modified Sooty Tern Golden Eagle Optimization (MSTGEO). The implementation results will be analyzed through existing DR detection schemes to ensure the efficiency of the suggested DR detection model.Öğe Diagnosing cervical cancer using machine learning methods(Institute of Electrical and Electronics Engineers Inc., 2022) Coşar Soğukkuyu, Derya Yeliz; Ata, OğuzPre-diagnosis of any kind of cancer type has critical impact on person's life. According to World Health Organization Cervical cancer is still one of the most common gynecologic cancers in the world that affects women life. if medical experts focus on early diagnosis of the disease which is detecting symptomatic patients as early as possible, patients will have the best chance for successful treatment since Cervical Cancer is preventable. When cancer treatment is delayed, mortality rate decreases, and treatment becomes more complicated and expensive. Currently, systems based on Artificial Intelligence are used for decision making Machine learning techniques let automated detection of cervical cancer run more quickly and efficiently. In this study a novel ensemble approach is presented to predict the risk of cervical cancer by developing hybrid machine learning model. Multiple performance measurements such as Accuracy, precision score, recall score, and F1 are performed to evaluate the novel model. The results indicate that the proposed novel model can be effectively used to pre diagnosis of Cervical cancer with accuracy 97%.Öğe Diagnosing coronary artery disease on the basis of hard ensemble voting optimization(2022) Mohammedqasim, Hayder; Mohammedqasim, Roa'a; Ata, Oğuz; Alyasin, Eman IbrahimBackground and Objectives: Recently, many studies have focused on the early diagnosis of coronary artery disease (CAD), which is one of the leading causes of cardiac-associated death worldwide. The effectiveness of the most important features influencing disease diagnosis determines the performance of machine learning systems that can allow for timely and accurate treatment. We performed a Hybrid ML framework based on hard ensemble voting optimization (HEVO) to classify patients with CAD using the Z-Alizadeh Sani dataset. All categorical features were converted to numerical forms, the synthetic minority oversampling technique (SMOTE) was employed to overcome imbalanced distribution between two classes in the dataset, and then, recursive feature elimination (RFE) with random forest (RF) was used to obtain the best subset of features. Materials and Methods: After solving the biased distribution in the CAD data set using the SMOTE method and finding the high correlation features that affected the classification of CAD patients. The performance of the proposed model was evaluated using grid search optimization, and the best hyperparameters were identified for developing four applications, namely, RF, AdaBoost, gradient-boosting, and extra trees based on an HEV classifier. Results: Five fold cross-validation experiments with the HEV classifier showed excellent prediction performance results with the 10 best balanced features obtained using SMOTE and feature selection. All evaluation metrics results reached > 98% with the HEV classifier, and the gradient-boosting model was the second best classification model with accuracy = 97% and F1-score = 98%. Conclusions: When compared to modern methods, the proposed method perform well in diagnosing coronary artery disease, and therefore, the proposed method can be used by medical personnel for supplementary therapy for timely, accurate, and efficient identification of CAD cases in suspected patients.Öğe e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model(Springer, 2024) Jasim, Abdulrahman Ahmed; Hazim, Layth Rafea; Mohammedqasim, Hayder; Mohammedqasem, Roa’a; Ata, Oğuz; Salman, Omar HusseinOne of the most fatal and serious diseases that humans have encountered is diabetes, an illness affecting thousands of individuals yearly. In this era of digital systems, diabetes prediction based on machine learning (ML) is gaining high momentum. One of the benefits of treating patients early in the course of their noncommunicable diseases (NCDs) is that they can avoid costly therapies when the illness worsens later in life. Incidentally, diabetes is complicated by the dearth of medical professionals in underserved areas, such as distant rural communities. In these situations, the Internet of Medical Things and machine learning (ML) models can be used to offer healthcare practitioners the necessary prediction tools to more effectively and timely make decisions, thus assisting the early identification and diagnosis of NCDs. In this study, four conventional and hyper-AdaBoost ML models were trained and tested on the PIMA Indian Diabetes dataset. Patients with diabetes were classified on the basis of laboratory findings. Pre-processing tasks, such as the handling of imbalanced data and missing values, were performed prior to feature importance and normalisation activities. The algorithm with the best performance was examined using precision, accuracy, F1, recall and area under the curve metrics. Then, all ML models were hyper parametrically tuned via grid search to optimise their performance and reduce their error times. The decision process was also evaluated to further enhance the models. The AdaBoost-ET model performed even when features were not selected for binary classification. The model proposed in this study can predict diabetes with unprecedented high accuracy compared with the models in previous studies.Öğe Efficient energy and QoS based routing algorithm for wireless body area network(IEEE, 2021) Ojelade, Afolabi K.; Ibrahim, Abdullahi Abdu; Ata, Oğuz—In this study, a Novel architecture comprising a sink node is designed to minimize power consumption as well as improve quality of service (QoS) of wireless body area network (WBAN). Furthermore, in the quest to enhance the network lifetime and QoS, we utilized MAC protocol to classify the physiological attributes into normal and critical data. A routing protocol known as Efficient Energy and QoS (EEQ) algorithm is implemented to transmit the normal data through the shortest and appropriate route. According to the EEQ algorithm, the weight value is considered based on parameters such as network lifetime, link stability and throughput. The data is conveyed through the path having the highest weight value. However, the data is encrypted using RSA algorithm. In order to compare EEQ with other existing protocols, a number of simulations is executed on OMNet++ software to show that the proposed work outperforms other related research works.Öğe Electronic health records system using blockchain technology(Special Issue on Current Trends in Management and Information Technology, 2021) Hasan, Q. H.; Yassin, Ali A.; Ata, OğuzBlockchain technology is one of the most important and disruptive technologies in the world. Nowadays the healthcare center needs to share patient databases over all departments of the healthcare centers. Although, electronic healthcare records overcome several problems compared with manual records, but still suffer from many issues such as security, the privacy of patient data overall as we should transfer over a database from a central database to a decentralized database. In this paper, we proposed a good security system to manage the data of patients based on blockchain technology and a decentralized database. Depending on decentralized database and blockchain. Our proposed system provides the secure exchange of patient data, reliability, and high efficiency in sharing data during transaction data network equivalence checking to perform this validation of patient information in the blockchain and healthcare centers.Öğe Enhancing IIOT security with machine learning and deep learning for intrusion detection(University Of Malaya, 2024) Awad, Omer Fawzi; Hazim, Laytha Rafea; Jasim, Abdulrahman Ahmed; Ata, OğuzThe rapid growth of the Internet of Things (IoT) and digital industrial devices has significantly impacted various aspects of life, underscoring the importance of the Industrial Internet of Things (IIoT). Given its importance in industrial contexts that affect human life, the IIoT represents a key subset of the broader IoT landscape. Due to the proliferation of sensors in smart devices, which are viewed as points of contact, as the gathering of data and information regarding the IIoT systems and devices operating on the IoT, there is an urgent requirement for developing effective security methods to counter such threats as well as protecting IIoT systems. In this study, we develop and evaluate a well -optimized intrusion detection system (IDS) based on deep learning (DL) and machine learning (ML) techniques to enhance IIoT security. Leveraging the Edge-IIoTset dataset, specifically designed for IIoT cybersecurity evaluations, we focus on detecting and mitigating 14 distinct attack types targeting IIoT and IoT protocols. These attacks are categorized into five threat groups: information collection, malware, DDoS, manin -the -middle attacks, and injection attacks. We conducted experiments using machine learning algorithms (knearest neighbors, decision tree) and a neural network on the KNIME platform, achieving a remarkable 100% accuracy with the decision tree model. This high accuracy demonstrates the effectiveness of our approach in protecting industrial IoT networks.Öğe Enhancing predictive performance in Covid-19 healthcare datasets: A case study based on hyper adasyn over-samplingand genetic feature selection(2024) Mohammedqasim, Hayder; Jasim, Abdulrahman Ahmed; Mohammedqasem, Roa'a; Ata, OğuzPredictive analytics is paramount in the health industry, where it finds its wide application, in that it helps increase the forecast's accuracy level based on big data. Most of the time, there is a tendency toward the imbalance of the datasets in healthcare. In this study, two COVID-19 datasets from Kaggle were used as a case study of dataset imbalance. In such scenarios of imbalanced datasets like COVID-19, conventional sampling methods like ADASYN (Adaptive Synthetic Sampling Approach for Imbalanced Learning) tend to yield only modest accuracy levels. To address another problem like finding the optimal features, this study proposes a novel approach that combines oversampling techniques with genetic feature selection (GFs) using laboratory data. This innovative method aims to construct machine -learning clinical prediction models for the identification of COVID-19 infected patients, leveraging two widely recognized datasets by using hyper ADASYN over -sampling and genetic feature selection, stands out for its unprecedented precision in identifying relevant features crucial for accurate predictions. Unlike the traditional approach, it can solve the class imbalance problem and tune the feature space to bring about a dramatic increase in accuracy, precision, recall, and overall predictive performance by using our hypermodel. Our approach significantly enhanced the performance of the classifier, and the Random Forest (RF) model with "n" trees classifies accurately to the limit of 99%, with precision 99%, recall 99%, and F1 -score 99% for each of the datasets. Decision Tree (DT) model achieved 92% with all metrics for Dataset I, and 95% with all metrics for Dataset II. Multilayer Perceptron (MLP) achieved 99% with all metrics, respectively, for both datasets. Gradient Boosting (XGB) achieved 97% for all metrics with dataset I and 98% with all metrics for dataset II. These results underscore the efficacy of our proposed method in balancing COVID-19 datasets and enhancing predictive accuracy.Öğe Enhancing self-care prediction in children with impairments: a novel framework for addressing imbalance and high dimensionality(2024) Alyasin, Eman Ibrahim; Ata, Oğuz; Mohammedqasim, Hayder; Mohammedqasem, Roa'aAddressing the challenges in diagnosing and classifying self-care difficulties in exceptional children's healthcare systems is crucial. The conventional diagnostic process, reliant on professional healthcare personnel, is time-consuming and costly. This study introduces an intelligent approach employing expert systems built on artificial intelligence technologies, specifically random forest, decision tree, support vector machine, and bagging classifier. The focus is on binary and multi-label SCADI datasets. To enhance model performance, we implemented resampling and data shuffling methods to tackle data imbalance and generalization issues, respectively. Additionally, a hyper framework feature selection strategy was applied, using mutual-information statistics and random forest recursive feature elimination (RF-RFE) based on a forward elimination method. Prediction performance and feature significance experiments, employing Shapley value explanation (SHAP), demonstrated the effectiveness of the proposed model. The framework achieved a remarkable overall accuracy of 99% for both datasets used with the fewest number of unique features reported in contemporary literature. The use of hyperparameter tuning for RF modeling further contributed to this significant improvement, suggesting its potential utility in diagnosing self-care issues within the medical industry.Öğe Enhancing the diagnosis of liver disease : combining machine learning with the Indian liver patient dataset(Springer Science and Business Media Deutschland GmbH, 2024) Alyasin, Eman Ibrahim; Ata, OğuzThus, this study illustrates a comprehensive examination of machine learning techniques for liver disease diagnosis using the Indian Liver Disease Patients Dataset (ILPD). In view of the critical need to identify liver disorders early and accurately, we used a multimodal machine learning approach involving feature selection, advanced preprocessing, and classifier integration. The use of stacking classifier with ExtraTrees at the meta level, and RF (Random Forest), XGBoost, DT (Decision Tree) and ExtraTrees at the base level is a novelty in our method. When combined with tenfold cross-validation, this technique facilitates extensive evaluation across various data partitions. In contrast to other works that have concentrated on minimizing data imbalances and increasing feature relevance to enhance model prediction accuracies; our work stands out as unique. There was an impressive improvement in accuracy precision and reliability as compared to previous models by our stacking classifier which achieved over 90% accuracy and an AUC score. This demonstration shows why it is necessary to combine several machine learning methods including their application within medical institutions. Also, our study compares itself with the latest researches on similar issues so as to show what has been done differently in our work.Öğe Enhancing the process of AES : a lightweight cryptography algorithm AES for AD-HOC environments(2022) Alhandhal, Mustafa; Abdullah, Alharith A.; Ata, Oğuz; Aydın, ÇağatayAd hoc networks have become more widespread and important because they support mobility and can be used in different situations and some difficult areas such as rescue missions, military, and vehicular communications. Security is stated among the most significant challenges facing Ad Hoc networks due to its characteristic features such as the topology dynamicity, lack of infrastructure centralization, and open architecture. Ad hoc networks work on battery power, and this kind of networks tend to consume more power and time to process data and memory resources through data encryption, eventually requiring a higher amount of power and more time. Therefore, an AES lightweight algorithm is proposed, which is one of the complicated encryption algorithms. This algorithm will be modified to improve the security as well as reduce the amount of time and energy consumed in comparison with the original algorithm. The enhanced version is based on the outputs of sub byte and shift row, in addition to an exclusive X-OR operator between them, whereby the output of the X-OR operator is added to the round key layer. The results confirm that this enhanced algorithm is efficient, accurate, and robust against multiple types of attacks related to ad hoc networks.Öğe Feature model configuration reuse scheme for self-adaptive systems(Tech Science Press, 2022) Alkubaisi, Sumaya; Ghoul, Said; Ata, OğuzMost large-scale systems including self-adaptive systems utilize feature models (FMs) to represent their complex architectures and benefit from the reuse of commonalities and variability information. Self-adaptive systems (SASs) are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite contexts. However, reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and resources. The process of configuration reuse can be a better alternative to some contexts to reduce computational time, effort and error-prone. Nevertheless, systems’ complexity can be reduced while the development process of systems by reusing elements or components. FMs are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system components. While current FM-based modelling techniques represent, manage, and reuse elementary features to model SASs concepts, modeling and reusing configurations have not yet been considered. In this context, this study presents an extension to FMs by introducing and managing configuration features and their reuse process. Evaluation results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.Öğe Genomic chronicle of SARS-CoV-2: A mutational analysis with over 1 million genome sequences(TÜBİTAK, 2021) Uğurel, Osman Mutluhan; Ata, Oğuz; Turgut Balık, DilekUse of information technologies to analyse big data on SARS-CoV-2 genome provides an insight for tracking variations and examining the evolution of the virus. Nevertheless, storing, processing, alignment and analyses of these numerous genomes are still a challenge. In this study, over 1 million SARS-CoV-2 genomes have been analysed to show distribution and relationship of variations that could enlighten development and evolution of the virus. In all genomes analysed in this study, a total of over 215M SNVs have been detected and average number of SNV per isolate was found to be 21.83. Single nucleotide variant (SNV) average is observed to reach 31.25 just in March 2021. The average variation number of isolates is increasing and compromising with total case numbers around the world. Remarkably, cytosine deamination, which is one of the most important biochemical processes in the evolutionary development of coronaviruses, accounts for 46% of all SNVs seen in SARS-CoV-2 genomes within 16 months. This study is one of the most comprehensive SARS-CoV-2 genomic analysis study in terms of number of genomes analysed in an academic publication so far, and reported results could be useful in monitoring the development of SARS-CoV-2.
