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https://www.sciencedirect.com/science/article/pii/S0957417408006283
In Garg and Gupta (2008) the authors propose an ensemble of support vector machine (SVM) where the different SVMs classifiers were trained with sequence features of bacterial virulent proteins such as amino acid compositions, 2-gram compositions, higher …Cited by: 17
https://www.researchgate.net/publication/222164370_An_ensemble_of_support_vector_machines_for_predicting_virulent_proteins
Nanni and Lumini [3] proposed an ensemble of SVM classifiers for the prediction of bacterial virulent proteins using features that were extracted directly from the amino acid sequence of a given ...
https://core.ac.uk/display/53376564
It is important to develop a reliable system for predicting bacterial virulent proteins for finding novel drug/vaccine and for understanding virulence mechanisms in pathogens. In this work we have proposed a bacterial virulent protein prediction method based on an ensemble of classifiers where the features are extracted directly from the amino acid sequence of a given protein.Cited by: 17
https://link.springer.com/article/10.1007/s00726-008-0083-0
Apr 22, 2008 · The success rate obtained by our system on a difficult dataset, where the sequences in a given membrane type have a low sequence identity to any other proteins of the same membrane type, are quite high, indicating that the proposed method, where the features are extracted directly from the amino acid sequence, is a feasible system for predicting the membrane protein type.Cited by: 29
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2220009/
An ensemble of Support Vector Machines outperforms a single Support Vector Machine trained on unbalanced data on the glycosylation site prediction task For each glycosylation type considered in this study, N-, O-, and C-linked glycosylation, we trained ensembles of Support Vector Machine (SVM) classifiers to predict whether or not a site in a protein sequence is a glycosylation site.
https://www.researchgate.net/publication/262327249_Ensemble_of_Diversely_Trained_Support_Vector_Machines_for_Protein_Fold_Recognition
Afterwards, by proposing an ensemble Support Vector Machines (SVM) which are diversely trained using features extracted from different physicochemical-based attributes, we enhance the protein fold...
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3292016/
In this paper, an accurate method, called AFP_PSSM, has been developed for predicting antifreeze proteins using a support vector machine (SVM) and position specific scoring matrix (PSSM) profiles. This is the first study in which evolutionary information in the form of PSSM profiles has been successfully used for predicting antifreeze proteins.Cited by: 21
https://www.researchgate.net/publication/5626083_VirulentPred_A_SVM_based_prediction_method_for_virulent_proteins_in_bacterial_pathogens
Machine learning algorithms for predicting virulent proteins have also been reported that apply SVM-based models based on AAC and DPC [16], an ensemble of SVMbased models trained with features ...
https://www.sciencedirect.com/science/article/pii/S0933365718303920
Through GA feature selection and Bagging ensemble learning algorithm, the prediction accuracy of druggable proteins is steadily improved. After the feature selection with the genetic algorithm, the ACC increases by 2.44%, the MCC increases by 4.99% and the …Cited by: 1
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217832/
Jan 06, 2017 · A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function,...Cited by: 54
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