Predicting antifreeze proteins with weighted generalized dipeptide composition and multi-regression feature selection ensemble

Background Antifreeze proteins (AFPs) are a group of proteins that inhibit body fluids from growing to ice crystals and thus improve biological antifreeze ability. It is vital to the survival of living organisms in extremely cold environments. However, little research is performed on sequences featu...

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Veröffentlicht in:	BMC bioinformatics Jg. 22; H. Suppl 3; S. 340 - 21
Hauptverfasser:	Wang, Shunfang, Deng, Lin, Xia, Xinnan, Cao, Zicheng, Fei, Yu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 23.06.2021 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accuracy Algorithms Amino acids Analysis Antifreeze proteins Antifreeze Proteins - genetics Antifreeze proteins prediction Bioinformatics Biomedical and Life Sciences Body fluids Classification Composition Computational Biology/Bioinformatics Computer Appl. in Life Sciences Crystal growth Crystals Datasets Decision trees Dipeptides Ensemble feature selection Experiments Feature extraction Feature selection Ice Ice crystals Lasso regression Life Sciences Machine learning Membrane proteins Membranes Methods Microarrays Optimization techniques Performance evaluation Predictions Principal components analysis Proteins Regression Reproducibility of Results Ridge regression Structure-function relationships Support Vector Machine Support vector machines Two-stage multiple regressions Weighted general dipeptide composition China Ridge regression Antifreeze proteins prediction Lasso regression Ensemble feature selection Two-stage multiple regressions Weighted general dipeptide composition
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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