ArticleThe Journal of membrane biology2016
iAFP-Ense: An Ensemble Classifier for Identifying Antifreeze Protein by Incorporating Grey Model and PSSM into PseAAC.
Article in The Journal of membrane biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 29 citations in OpenAlex.
- BERT-DomainAFP: Antifreeze protein recognition and classification model based on BERT and structural domain annotation.iScience · 2025Article
- Analysis of Ice-Binding Protein Evolution.Methods in molecular biology (Clifton, N.J.) · 2024Article
- PSSMCOOL: a comprehensive R package for generating evolutionary-based descriptors of protein sequences from PSSM profiles.Biology methods & protocols · 2022Article
- Cold adaptation strategies in plants-An emerging role of epigenetics and antifreeze proteins to engineer cold resilient plants.Frontiers in genetics · 2022Review
- Predicting antifreeze proteins with weighted generalized dipeptide composition and multi-regression feature selection ensemble.BMC bioinformatics · 2021Article
- ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs.Frontiers in bioengineering and biotechnology · 2021Article
- Antifreeze Proteins and Their Practical Utilization in Industry, Medicine, and Agriculture.Biomolecules · 2020Review
- AFP-LSE: Antifreeze Proteins Prediction Using Latent Space Encoding of Composition of k-Spaced Amino Acid Pairs.Scientific reports · 2020Article
- Draft genome sequences of bacteria isolated from the Deschampsia antarctica phyllosphere.Extremophiles : life under extreme conditions · 2018Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antifreeze proteins (AFPs), known as thermal hysteresis proteins, are ice-binding proteins. AFPs have been found in many fields such as in vertebrates, invertebrates, plants, bacteria, and fungi. Although the function of AFPs is common, the sequences and structures of them show a high degree of diversity. AFPs can be adsorbed in ice crystal surface and inhibit the growth of ice crystals in solution. However, the interaction between AFPs and ice crystal is not completely known for human beings. It is vitally significant to propose an automated means as a high-throughput tool to timely identify the AFPs. Analyzing physicochemical characteristics of AFPs sequences is very significant to understand the ice-protein interaction. In this manuscript, a predictor called "iAFP-Ense" was developed. The operation engine to run the AFPs prediction is an ensemble classifier formed by a voting system to fuse eleven different random forest classifiers based on feature extraction. We also compare our predictor with the AFP-PseAAC via the tenfold cross-validation on the same benchmark dataset. The comparison with the existing methods indicates the new predictor is very promising, meaning that many important key features which are deeply hidden in complicated protein sequences. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/iAFP-Ense .
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