ArticleScientific reports2022
Prediction of antifreeze proteins using machine learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Discovering naturally occurring antifreeze peptides from microbiome by integrating protein language models and molecular dynamics simulation.Journal of materials chemistry. B · 2026Article
- IRDRM: Exploring Biological Traits and Clinical Significance in Breast Cancer Through Gene Pairs and Somatic Mutation Profiles.Interdisciplinary sciences, computational life sciences · 2026Article
- A generative explainable model for antimicrobial peptide prediction using bidirectional temporal convolutional neural network.Scientific reports · 2026Article
- DeepStackVEGF a stacking ensemble deep learning framework for vascular endothelial growth factor prediction.Scientific reports · 2026Article
- SPPIPred: Stacking-based ensemble learning model for identification of protein-protein interaction.PloS one · 2026Article
- A deep learning model for epidermal growth factor receptor prediction using ensemble residual convolutional neural network.Scientific reports · 2025Article
- BERT-DomainAFP: Antifreeze protein recognition and classification model based on BERT and structural domain annotation.iScience · 2025Article
- Prediction and validation of nanowire proteins inComputational and structural biotechnology journal · 2025Article
- Deep-GB: A novel deep learning model for globular protein prediction using CNN-BiLSTM architecture and enhanced PSSM with trisection strategy.IET systems biology · 2024Article
- Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques.Scientific reports · 2024Article
- VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation.Briefings in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Living organisms including fishes, microbes, and animals can live in extremely cold weather. To stay alive in cold environments, these species generate antifreeze proteins (AFPs), also referred to as ice-binding proteins. Moreover, AFPs are extensively utilized in many important fields including medical, agricultural, industrial, and biotechnological. Several predictors were constructed to identify AFPs. However, due to the sequence and structural heterogeneity of AFPs, correct identification is still a challenging task. It is highly desirable to develop a more promising predictor. In this research, a novel computational method, named AFP-LXGB has been proposed for prediction of AFPs more precisely. The information is explored by Dipeptide Composition (DPC), Grouped Amino Acid Composition (GAAC), Position Specific Scoring Matrix-Segmentation-Autocorrelation Transformation (Sg-PSSM-ACT), and Pseudo Position Specific Scoring Matrix Tri-Slicing (PseTS-PSSM). Keeping the benefits of ensemble learning, these feature sets are concatenated into different combinations. The best feature set is selected by Extremely Randomized Tree-Recursive Feature Elimination (ERT-RFE). The models are trained by Light eXtreme Gradient Boosting (LXGB), Random Forest (RF), and Extremely Randomized Tree (ERT). Among classifiers, LXGB has obtained the best prediction results. The novel method (AFP-LXGB) improved the accuracies by 3.70% and 4.09% than the best methods. These results verified that AFP-LXGB can predict AFPs more accurately and can participate in a significant role in medical, agricultural, industrial, and biotechnological fields.
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Registered trials
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