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Comprehensive Assessment of Machine Learning-based Methods for Predicting Antimicrobial Peptides by Jing Xu; Fuyi Li; André Leier; Dongxu Xiang; Hsin-Hui Shen; Tatiana T Marquez Lago; Jian Li; Dong-Jun Yu; Jiangning Song is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is Comprehensive Assessment of Machine Learning-based Methods for Predicting Antimicrobial Peptides about?
## Abstract Antimicrobial peptides (AMPs) are a unique and diverse group of molecules that play a crucial role in a myriad of biological processes and cellular functions. AMP-related studies have become increasingly popular in recent years due to antimicrobial resistance, which is becoming an emerging global concern. Systematic experimental identification of AMPs faces many difficulties due to the limitations of current methods. Given its significance, more than 30 computational methods have been developed for accurate prediction of AMPs. These approaches show high diversity in their data set size, data quality, core algorithms, feature extraction, feature selection techniques and evaluation strategies. Here, we provide a comprehensive survey on a variety of current approaches for AMP identification and point at the differences between these methods. In addition, we evaluate the predictive performance of the surveyed tools based on an independent test data set containing 1536 AMPs and 1536 non-AMPs. Furthermore, we construct six validation data sets based on six different common AMP databases and compare different computational methods based on these data sets. The results indicate
Who reads Comprehensive Assessment of Machine Learning-based Methods for Predicting Antimicrobial Peptides?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Jing Xu; Fuyi Li; André Leier; Dongxu Xiang; Hsin-Hui Shen; Tatiana T Marquez Lago; Jian Li; Dong-Jun Yu; Jiangning Song
- Publisher
- Oxford University Press (OUP)
- Published
- 2021
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)