A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides
https://orcid.org/0000-0002-0914-1577
Abstract Viruses have killed and infected millions of people across the world. It causes several chronic diseases like COVID-19, HIV, and hepatitis. To cope with such diseases and virus infections, antiviral peptides (AVPs) have been applied in the design of drugs. Keeping in view the significant role in pharmaceutical industry and other research fields, identification of AVPs is highly indispensable. In this connection, experimental and computational methods were proposed to identify AVPs. However, more accurate predictors for boosting AVPs identification are highly desirable. This work presents a thorough study and reports the available predictors of AVPs. We explained applied datasets, feature representation approaches, classification algorithms, and evaluation parameters of performance. In this study, the limitations of the existing studies and the best methods were emphasized. Provided the pros and cons of the applied classifiers. The future insights demonstrate efficient feature encoding approaches, best feature optimization schemes, and effective classification techniques that can improve the performance of novel method for accurate prediction of AVPs.
Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides
https://orcid.org/0000-0002-0914-1577
Abstract Viruses have killed and infected millions of people across the world. It causes several chronic diseases like COVID-19, HIV, and hepatitis. To cope with such diseases and virus infections, antiviral peptides (AVPs) have been applied in the design of drugs. Keeping in view the significant role in pharmaceutical industry and other research fields, identification of AVPs is highly indispensable. In this connection, experimental and computational methods were proposed to identify AVPs. However, more accurate predictors for boosting AVPs identification are highly desirable. This work presents a thorough study and reports the available predictors of AVPs. We explained applied datasets, feature representation approaches, classification algorithms, and evaluation parameters of performance. In this study, the limitations of the existing studies and the best methods were emphasized. Provided the pros and cons of the applied classifiers. The future insights demonstrate efficient feature encoding approaches, best feature optimization schemes, and effective classification techniques that can improve the performance of novel method for accurate prediction of AVPs.
Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides
https://orcid.org/0000-0002-0914-1577
Abstract Viruses have killed and infected millions of people across the world. It causes several chronic diseases like COVID-19, HIV, and hepatitis. To cope with such diseases and virus infections, antiviral peptides (AVPs) have been applied in the design of drugs. Keeping in view the significant role in pharmaceutical industry and other research fields, identification of AVPs is highly indispensable. In this connection, experimental and computational methods were proposed to identify AVPs. However, more accurate predictors for boosting AVPs identification are highly desirable. This work presents a thorough study and reports the available predictors of AVPs. We explained applied datasets, feature representation approaches, classification algorithms, and evaluation parameters of performance. In this study, the limitations of the existing studies and the best methods were emphasized. Provided the pros and cons of the applied classifiers. The future insights demonstrate efficient feature encoding approaches, best feature optimization schemes, and effective classification techniques that can improve the performance of novel method for accurate prediction of AVPs.
Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides
Ali, Farman (author) / Kumar, Harish (author) / Alghamdi, Wajdi (author) / Kateb, Faris A. (author) / Alarfaj, Fawaz Khaled (author)
2023
Article (Journal)
Electronic Resource
English
Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides
| Springer Verlag | 2023
Recent advances in deep learning for traffic probabilistic prediction
| Taylor & Francis Verlag | 2024
Recent Developments in Machine Learning and Flyrock Prediction
| Springer Verlag | 2022