ArticleHeliyon2018
afpCOOL: A tool for antifreeze protein prediction.
Article in Heliyon, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 25 citations in OpenAlex.
- BERT-DomainAFP: Antifreeze protein recognition and classification model based on BERT and structural domain annotation.iScience · 2025Article
- SaGP: identifying plant saline-alkali tolerance genes based on machine learning techniques.Frontiers in plant science · 2025Article
- Analysis of Ice-Binding Protein Evolution.Methods in molecular biology (Clifton, N.J.) · 2024Article
- A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions.Frontiers in plant science · 2024Review
- Review
- Prediction and analysis of antifreeze proteins.Heliyon · 2021Article
- Identifying Antifreeze Proteins Based on Key Evolutionary Information.Frontiers in bioengineering and biotechnology · 2020Article
- Water in protein hydration and ligand recognition.Journal of molecular recognition : JMR · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Various cold-adapted organisms produce antifreeze proteins (AFPs), which prevent the freezing of cell fluids by inhibiting the growth of ice crystals. AFPs are currently being recognized in various organisms, living in extremely low temperatures. AFPs have several important applications in increasing freeze tolerance of plants, maintaining the tissue in frozen conditions and producing cold-hardy plants by applying transgenic technology. Substantial differences in the sequence and structure of the AFPs, pose a challenge for researchers to identify these proteins. In this paper, we proposed a novel method to identify AFPs, using supportive vector machine (SVM) by incorporating 4 types of features. Results of the two used benchmark datasets, revealed the strength of the proposed method in AFP prediction. According to the results of an independent test setup, our method outperformed the current state-of-the-art methods. In addition, the comparison results of the discrimination power of different feature types revealed that physicochemical descriptors are the most contributing features in AFP detection. This method has been implemented as a stand-alone tool, named afpCOOL, for various operating systems to predict AFPs with a user friendly graphical interface.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.