COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus

Abstract

This paper proposes an efficient and accurate method to predict coronavirus disease 19 (COVID-19) based on the genome similarity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and a bat SARS-CoV-like coronavirus. We introduce similarity features to distinguish COVID-19 from other human coronaviruses by comparing human coronaviruses with a bat SARS-CoV-like coronavirus. In the proposed method each human coronavirus sequence is assigned to three similarity scores considering nucleotide similarities and mutations that lead to the strong absence of cytosine and guanine nucleotides. Next the proposed features are integrated with CpG island features of the genome sequences to improve COVID-19 prediction. Thus each genome sequence is represented by five real numbers. We exhibit the effectiveness of the proposed features using six machine learning classifiers on a dataset including the genome sequences of human coronaviruses similar to SARS-CoV-2. The performances of the machine learning classifiers are close to each other and k-nearest neighbor classifier with similarity features achieves the best results with an accuracy of 99.2%. Moreover, k-nearest neighbor classifier with the integration of CpG based and similarity features has an admirable performance and achieves an accuracy of 99.8%. Experimental results demonstrate that similarity features remarkably decreases the number of false negatives and significantly improve the overall performance. The superiority of the proposed method is also highlighted by comparing with the state-of-the-art studies detecting COVID-19 from genome sequences.

On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study

“…Nevertheless, shortterm forecastings are also important, especially in supporting operational decisions during the COVID-19 pandemic. Thus, classical parametric and machine learning models have also gained space during the pandemic, such as Autoregressive Integrated Moving Average (ARIMA) [21,[31][32][33][34][35][36][37], Holt-Winters [35][36][37][38][39][40], Prophet [20,36,[40][41][42], K-Nearest Neighbors (KNN) Regressor [37,[43][44][45], Random Forest Regressor (RFR) [11,16,46,47], and Support Vector Regressor (SVR) [16,37,40,[47][48][49]. Researchers may also choose two models [40,[43][44][45]47] or more than three models [16,36,37] to make the forecasts.…”

“…However, MAPE has the notable limitation of resulting in infinite or undefined values for zero or close-to-zero values [51,53,54]. Other metrics used by researchers to assess the performance of machine learning methods are R2, R2 adjusted, precision, recall, F1-score, and accuracy, or the Matthews Correlation Coefficient (MCC) and the area under the receiver operating characteristic (ROC) curve, also known as AUC [44,48,55]. A forecasting method that has the R2, R2 adjusted, F1-score, or AUC closest to 1 is the one that should be chosen.…”

“…The K-Nearest Neighbors (KNN) technique is a nonparametric and lazy learning classification method [37,44]. Initially, the KNN was designed to accomplish classification problems.…”

“…Notwithstanding, decades after the KNN's original conceptualizations, around the early 1990s, researchers began investigating it for regression goals [76]. Instead of learning the training dataset, KNN does not need a training phase and holds the training dataset [44].…”

“…Next, the algorithm randomly creates many decision trees with "boot-strap samples" from the data [11,44,46]. Subsequently, the randomly selected predictors define the branching of each tree at node points.…”