ArticleFrontiers in bioengineering and biotechnology2020
Predicting Cell Wall Lytic Enzymes Using Combined Features.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Thermostability engineering of microbial transglutaminase using artificial intelligence and investigation of its underlying mechanisms.Bioresources and bioprocessing · 2026Article
- PBIP: a deep learning framework for predicting phage-bacterium interactions at the strain level.Briefings in bioinformatics · 2025Article
- Article
- Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning.Translational psychiatry · 2022Article
- SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using sequence-derived information.Computational and structural biotechnology journal · 2022Article
- Biological Sequence Classification: A Review on Data and General Methods.Research (Washington, D.C.) · 2022Article
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Authors and funding
2 authors.
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Abstract
Due to the overuse of antibiotics, people are worried that existing antibiotics will become ineffective against pathogens with the rapid rise of antibiotic-resistant strains. The use of cell wall lytic enzymes to destroy bacteria has become a viable alternative to avoid the crisis of antimicrobial resistance. In this paper, an improved method for cell wall lytic enzymes prediction was proposed and the amino acid composition (AAC), the dipeptide composition (DC), the position-specific score matrix auto-covariance (PSSM-AC), and the auto-covariance average chemical shift (acACS) were selected to predict the cell wall lytic enzymes with support vector machine (SVM). In order to overcome the imbalanced data classification problems and remove redundant or irrelevant features, the synthetic minority over-sampling technique (SMOTE) was used to balance the dataset. The F-score was used to select features. The S
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