Hamlet-Pattern-Based Automated COVID-19 and Influenza Detection Model Using Protein Sequences

Abstract

SARS-CoV-2 and Influenza-A can present similar symptoms. Computer-aided diagnosis can help facilitate screening for the two conditions, and may be especially relevant and useful in the current COVID-19 pandemic because seasonal Influenza-A infection can still occur. We have developed a novel text-based classification model for discriminating between the two conditions using protein sequences of varying lengths. We downloaded viral protein sequences of SARS-CoV-2 and Influenza-A with varying lengths (all 100 or greater) from the NCBI database and randomly selected 16,901 SARS-CoV-2 and 19,523 Influenza-A sequences to form a two-class study dataset. We used a new feature extraction function based on a unique pattern, HamletPat, generated from the text of Shakespeare’s Hamlet, and a signum function to extract local binary pattern-like bits from overlapping fixed-length (27) blocks of the protein sequences. The bits were converted to decimal map signals from which histograms were extracted and concatenated to form a final feature vector of length 1280. The iterative Chi-square function selected the 340 most discriminative features to feed to an SVM with a Gaussian kernel for classification. The model attained 99.92% and 99.87% classification accuracy rates using hold-out (75:25 split ratio) and five-fold cross-validations, respectively. The excellent performance of the lightweight, handcrafted HamletPat-based classification model suggests that it can be a valuable tool for screening protein sequences to discriminate between SARS-CoV-2 and Influenza-A infections.

An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

“…The main classifier shows an average recognition time of 61 milliseconds, which is consistent with the findings of the study by [4]. Furthermore, the proposed approach has proved to be more efficient in handling a broader range of sediment classes compared to alternative methods such as [3], [4], [5], and [44]. Due to morphological similarities, it is hard to distinguish between RBCs, WBCs, Squamous Epithelial Cells, Non-Squamous Epithelial Cells, hyaline casts, and unknown Casts.…”

“…A key strength of the proposed method lies in its adept handling of missing data and effective resolution of class imbalance. In contrast, previous research [5], [44] has underscored that grappling with missing data and class imbalance could introduce biases and potentially result in low recognition performance. The proficient management of missing data is a notable achievement facilitated through the utilization of the proposed datacentric approach.…”

“…The proposed method exhibits clear advantages compared to state-of-the-art, as illustrated in Table 5. It can be seen that the proposed method's accuracy is a little lower [3]; however, it exceeds [4], [5], [45], [18] and [44]. It is worth noting that the methodologies proposed in [3] can successfully handle only 7 out of the 12 categories in the given recognition challenge.…”

Editorial on Special Issue “Medical Data Processing and Analysis”

“…The model also shows high specificity in detecting viral proteins, with a specificity of 99.99% for COVID-19 and 99.96% for influenza viruses. The proposed model can be used in clinical settings for early detection and diagnosis of COVID-19 and influenza viruses [ 7 ]. However, the genomic and tabular data with recurrent neural networks are employed to predict type-2 diabetes.…”