# AMP-GSM: Prediction of Antimicrobial Peptides via a Grouping-Scoring-Modeling Approach
## 4 Discussion  
In this study, we propose AMP-GSM, a novel approach that is built on the grouping and ranking of peptide features. The method relies on grouping the features according to their biological characteristics, and then scoring those groups according to their importance in terms of distinguishing antimicrobial peptides from non-antimicrobial peptides. Traditional methods often use the properties of antimicrobial peptides together rather than grouping them. On the other hand, feature selection methods select the features that they \begin{table}
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**Method** & **Evaluation Set** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** \\ \hline AIPpred & 5-Fold CV & 0.73 & 0.75 & 0.71 & 0.80 \\ ERT & 5-Fold CV & 0.73 & 0.73 & 0.72 & 0.79 \\ SVM & 5-Fold CV & 0.65 & 0.64 & 0.67 & 0.70 \\ k-NN & 5-Fold CV & 0.64 & 0.51 & 0.77 & 0.69 \\ CMIM-LogitBoost & 5-Fold MCCV & 0.54 & 0.67 & 0.40 & 0.55 \\ IG-AdaBoost & 5-Fold MCCV & 0.69 & 0.66 & 0.72 & 0.73 \\ mRMR-LogitBoost & 5-Fold MCCV & 0.50 & 0.79 & 0.20 & 0.50 \\ AMP-GSM Model & 5-Fold MCCV & **0.99** & **1** & **0.99** & **1** \\ \hline \hline \end{tabular}
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Table 10: Performance evaluation of AMP-GSM with other traditional feature selection and classification models for Dataset 3 [35]. \begin{table}
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**Method** & **Evaluation** & **Sensitivity (\%)** & **Specificity (\%)** & **Accuracy (\%)** & **AUC (\%)** & **F1 Measure** \\ \hline DNN Model & 10-fold CV & 88.81 (\(\pm\)3.53) & **94.21 (\(\pm\)2.68)** & 91.51 (\(\pm\)0.89) & 96.58 (\(\pm\)0.66) & - \\ CMIM-DT & 10-fold MCCV & 51.34 \(\pm\) 0.17 & 51.40 \(\pm\) 0.17 & 51.37 \(\pm\) 0.03 & 51.37 \(\pm\) 0.03 & 50.02 \(\pm\) 0.09 \\ IG-RF & 10-fold MCCV & 88.70 \(\pm\) 0.02 & 91.40 \(\pm\) 0.02 & 90.05 \(\pm\) 0.01 & 96.36 \(\pm\) 0.007 & 89.91 \(\pm\) 0.01 \\ mRMR-RF & 10-fold MCCV & 33.70 \(\pm\) 0.18 & 67.41 \(\pm\) 0.21 & 50.56 \(\pm\) 0.03 & 50.80 \(\pm\) 0.05 & 37.80 \(\pm\) 0.14 \\ AMP-GSM Model & 10-fold MCCV & **91.01 (\(\pm\)0.23)** & 92.97(\(\pm\)0.03) & **91.71 (\(\pm\)0.13)** & **97.07 (\(\pm\)0.06)** & **91.59 (\(\pm\)0.15)** \\ \hline \hline \end{tabular}
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Table 9: Performance evaluation of AMP-GSM with a DNN model for Dataset 2 [33]. identify as important, and then develop the classification models using the selected features. However, such a selection is not a group-based selection. Based on all the attributes, traditional feature selection methods select the most important ones. Studies in this area are mostly aimed at classification by taking feature groups individually or collectively, or by using a set of features selected by traditional feature selection methods [59; 60; 61]. In this study, structure-based, sequence-based, and physico-chemical features were grouped and their effects on classification performance were evaluated. We analyzed a comprehensive set of features, including amino acid composition, dipeptide composition, pseudo amino acid composition, CTD of physico-chemical properties, various autocorrelations, quasi-sequence-order descriptors, and sequence order coupling number. These features are generated for each peptide within the Gram-positive and Gram-negative datasets separately. Separately for the Gram-positive and Gram-negative datasets, we compared the performances of the models that apply the proposed AMP-GSM technique and alternative feature selection strategies. As shown in Figures 3 and 4, AMP-GSM resulted in higher AUC values on both Gram-negative and Gram-positive dataset. An AMP prediction system has a very high number of potential input features, and the decisions made regarding which features to use for antimicrobial prediction greatly affect prediction performance in terms of accuracy and AUC. Finding novel antimicrobial descriptors that may be connected to physico-chemical properties could reduce the wide accuracy range of the prediction algorithms, and aid in identifying the real significance of characteristics related to antimicrobial activities.