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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Vydáno v:	BMC bioinformatics Ročník 22; číslo Suppl 3; s. 340 - 21
Hlavní autoři:	Wang, Shunfang, Deng, Lin, Xia, Xinnan, Cao, Zicheng, Fei, Yu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 23.06.2021 BioMed Central Ltd Springer Nature B.V BMC
Témata:	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
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Shrnutí:	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 feature extraction and selection for antifreeze proteins classification in the structure and function prediction, which is of great significance. Results In this paper, to predict the antifreeze proteins, a feature representation of weighted generalized dipeptide composition (W-GDipC) and an ensemble feature selection based on two-stage and multi-regression method (LRMR-Ri) are proposed. Specifically, four feature selection algorithms: Lasso regression, Ridge regression, Maximal information coefficient and Relief are used to select the feature sets, respectively, which is the first stage of LRMR-Ri method. If there exists a common feature subset among the above four sets, it is the optimal subset; otherwise we use Ridge regression to select the optimal subset from the public set pooled by the four sets, which is the second stage of LRMR-Ri. The LRMR-Ri method combined with W-GDipC was performed both on the antifreeze proteins dataset (binary classification), and on the membrane protein dataset (multiple classification). Experimental results show that this method has good performance in support vector machine (SVM), decision tree (DT) and stochastic gradient descent (SGD). The values of ACC, RE and MCC of LRMR-Ri and W-GDipC with antifreeze proteins dataset and SVM classifier have reached as high as 95.56%, 97.06% and 0.9105, respectively, much higher than those of each single method: Lasso, Ridge, Mic and Relief, nearly 13% higher than single Lasso for ACC. Conclusion The experimental results show that the proposed LRMR-Ri and W-GDipC method can significantly improve the accuracy of antifreeze proteins prediction compared with other similar single feature methods. In addition, our method has also achieved good results in the classification and prediction of membrane proteins, which verifies its widely reliability to a certain extent.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-021-04251-z