Predictive analysis for pathogenicity classification of H5Nx avian influenza strains using machine learning techniques

Chadha, Akshay; Dara, Rozita; Pearl, David L.; Sharif, Shayan; Poljak, Zvonimir

doi:10.1016/j.prevetmed.2023.105924

Cited by 4 publications

(1 citation statement)

References 55 publications

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“…In the past decades of research on influenza virus, machine learning methods have been used to infer certain pathogenic markers in the hemagglutinin (HA) gene [14] . To classify the pathogenicity classification of H5Nx avian influenza strains, Akshay Chadha et al [15]compared the performance of different machine learning classifiers such as logistic regression (LR) with lasso and ridge regularization, random forest (RF), K-nearest neighbor (KNN), naive Bayes (NB), support vector machine (SVM) and convolutional neural networks (CNN). Edyta Swiezton et al [16] used Bayesian methods for phylogenetic and molecular analysis of H5N8 and H5N5 viruses.…”

Section: Introductionmentioning

confidence: 99%

A new alignment-free method: Subsequence Correlation Coefficient Vector(SCCFV) for influenza A comparison using virus genomes

He,

Yu,

et al. 2024

Preprint

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Influenza viruses remain a formidable threat to global public health due to their high mutability and infectivity. Accurate prediction of influenza virus subtypes is crucial for clinical treatment and disease prevention. In recent years, machine learning methods have played an important role in studying influenza viruses. This study proposes a new alignment-free method based on the correlation of k-grams called Subsequence Correlation Coefficient Vector (SCCFV) to subtype hemagglutinin (HA) and neuraminidase (NA) of influenza virus. In the method, each influenza virus sequence is converted to four time series and the correlation coefficients of time series are utilized to extract the features of sequences. Then the supervised learning methods are used for the subtype classification of influenza viruses. We compare the effectiveness of the random forest, decision tree and support vector machine classifiers. Experimental results show that the random forest method achieves the best performance with an accuracy of 0.99979, an precision of 0.99996 and a recall of 0.99997. All prediction indicators of our method are significantly higher than traditional methods.

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Section: Introductionmentioning

confidence: 99%

A new alignment-free method: Subsequence Correlation Coefficient Vector(SCCFV) for influenza A comparison using virus genomes

He,

Yu,

et al. 2024

Preprint

View full text Add to dashboard Cite

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Optimizing classification efficiency with machine learning techniques for pattern matching

Hamed¹,

Ibrahim²,

El‐Hafeez³

2023

J Big Data

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The study proposes a novel model for DNA sequence classification that combines machine learning methods and a pattern-matching algorithm. This model aims to effectively categorize DNA sequences based on their features and enhance the accuracy and efficiency of DNA sequence classification. The performance of the proposed model is evaluated using various machine learning algorithms, and the results indicate that the SVM linear classifier achieves the highest accuracy and F1 score among the tested algorithms. This finding suggests that the proposed model can provide better overall performance than other algorithms in DNA sequence classification. In addition, the proposed model is compared to two suggested algorithms, namely FLPM and PAPM, and the results show that the proposed model outperforms these algorithms in terms of accuracy and efficiency. The study further explores the impact of pattern length on the accuracy and time complexity of each algorithm. The results show that as the pattern length increases, the execution time of each algorithm varies. For a pattern length of 5, SVM Linear and EFLPM have the lowest execution time of 0.0035 s. However, at a pattern length of 25, SVM Linear has the lowest execution time of 0.0012 s. The experimental results of the proposed model show that SVM Linear has the highest accuracy and F1 score among the tested algorithms. SVM Linear achieved an accuracy of 0.963 and an F1 score of 0.97, indicating that it can provide the best overall performance in DNA sequence classification. Naive Bayes also performs well with an accuracy of 0.838 and an F1 score of 0.94. The proposed model offers a valuable contribution to the field of DNA sequence analysis by providing a novel approach to pre-processing and feature extraction. The model’s potential applications include drug discovery, personalized medicine, and disease diagnosis. The study’s findings highlight the importance of considering the impact of pattern length on the accuracy and time complexity of DNA sequence classification algorithms.

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Avian Influenza: Lessons from the Past Outbreaks and Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to tackle the ongoing outbreak

Musa,

Nia,

Bragazzi

et al. 2024

Preprint

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Predictive analysis for pathogenicity classification of H5Nx avian influenza strains using machine learning techniques

Cited by 4 publications

References 55 publications

A new alignment-free method: Subsequence Correlation Coefficient Vector(SCCFV) for influenza A comparison using virus genomes

A new alignment-free method: Subsequence Correlation Coefficient Vector(SCCFV) for influenza A comparison using virus genomes

Optimizing classification efficiency with machine learning techniques for pattern matching

Avian Influenza: Lessons from the Past Outbreaks and Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to tackle the ongoing outbreak

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