ArticleThe Journal of membrane biology2015
TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition.
Article in The Journal of membrane biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- 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
- Prediction of antifreeze proteins using machine learning.Scientific reports · 2022Article
- Cold adaptation strategies in plants-An emerging role of epigenetics and antifreeze proteins to engineer cold resilient plants.Frontiers in genetics · 2022Review
- Prediction and analysis of antifreeze proteins.Heliyon · 2021Article
- Predicting antifreeze proteins with weighted generalized dipeptide composition and multi-regression feature selection ensemble.BMC bioinformatics · 2021Article
- Antifreeze Proteins and Their Practical Utilization in Industry, Medicine, and Agriculture.Biomolecules · 2020Review
- Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach.Frontiers in genetics · 2020Article
- Identifying Antifreeze Proteins Based on Key Evolutionary Information.Frontiers in bioengineering and biotechnology · 2020Article
- Prediction of protein structural classes by different feature expressions based on 2-D wavelet denoising and fusion.BMC bioinformatics · 2019Article
- Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.The Journal of membrane biology · 2016Article
- Inhibition of ice recrystallization and cryoprotective activity of wheat proteins in liver and pancreatic cells.Protein science : a publication of the Protein Society · 2016Article
- Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.The Journal of membrane biology · 2016Article
- iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.Molecular genetics and genomics : MGG · 2016Article
- Common protein sequence signatures associate with Sclerotinia borealis lifestyle and secretion in fungal pathogens of the Sclerotiniaceae.Frontiers in plant science · 2015Article
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Authors and funding
7 authors.
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Abstract
Antifreeze proteins (AFPs) are indispensable for living organisms to survive in an extremely cold environment and have a variety of potential biotechnological applications. The accurate prediction of antifreeze proteins has become an important issue and is urgently needed. Although considerable progress has been made, AFP prediction is still a challenging problem due to the diversity of species. In this study, we proposed a new sequence-based AFP predictor, called TargetFreeze. TargetFreeze utilizes an enhanced feature representation method that weightedly combines multiple protein features and takes the powerful support vector machine as the prediction engine. Computer experiments on benchmark datasets demonstrate the superiority of the proposed TargetFreeze over most recently released AFP predictors. We also implemented a user-friendly web server, which is openly accessible for academic use and is available at http://csbio.njust.edu.cn/bioinf/TargetFreeze. TargetFreeze supplements existing AFP predictors and will have potential applications in AFP-related biotechnology fields.
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