# AMP-GSM: Prediction of Antimicrobial Peptides via a Grouping-Scoring-Modeling Approach
## 3 Results
### Performance Evaluation of AMP-GSM on the Gram-Positive Dataset in Dataset 1  
The Gram-positive dataset in Dataset 1 includes 165 positively labeled (AMP) and 194 negatively labeled (non-AMP) samples. Average 100-fold MCCV performance metrics of AMP-GSM for the combined top 10 groups for the Gram-positive dataset are shown in Table 3. The first column represents the number of groups, and the second column shows the number of cumulative features. The performance of the top-ranked group is given in the last row, where number of groups = 1. Using 10 features on average, we obtained 92% for accuracy and 98% for AUC metric. The features from the initial top-ranked group and the second-highest scoring group are combined. The tenth row of Table 3 where number of groups = 2, displays the performance metrics derived for the top 2 groups cumulatively. AMP-GSM reports the cumulative performance metrics for the top 10 groups.  
It is worth noting that both for the Gram-negative and Gram-positive datasets of Dataset 1, all members of the top scoring group originated from the physico-chemical group, and this group showed the highest performance results. It was observed that the scoring made using only physico-chemical features obtained much better results than the groups formed by adding other features.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**\#Groups** & \
\begin{tabular}{l} **\#Features** \\ **(Mean)** \\ \end{tabular} & **Acc (Mean)** & **Sn (Mean)** & **Sp (Mean)** &
\begin{tabular}{l} **F-Measure** \\ **(Mean)** \\ \end{tabular} & **AUC (Mean)** & **Pr (Mean)** \\ \hline
6 & 439.45 & 0.92 & 0.93 & 0.91 & 0.91 & 0.98 & 0.90 \\ \hline
5 & 306.47 & 0.92 & 0.94 & 0.90 & 0.91 & 0.98 & 0.89 \\ \hline
4 & 190.91 & 0.92 & 0.94 & 0.91 & 0.91 & 0.98 & 0.90 \\ \hline
3 & 121.25 & 0.93 & 0.95 & 0.91 & 0.92 & 0.98 & 0.90 \\ \hline
2 & 60.6 & 0.93 & 0.95 & 0.92 & 0.93 & 0.99 & 0.91 \\ \hline
1 & 10 & 0.95 & 0.98 & 0.93 & 0.95 & 0.99 & 0.92 \\ \hline \hline \end{tabular}  
* The average number of features is shown in the column #Features. Firstly, the features from the top group are used to create a model, which is then tested using the testing part of the data. Secondly, the top one and two groups are used to create a model, which is subsequently tested. Similarly, the model is created using the features from the top 10 groups, and it is then tested. Acc: Accuracy, Sn: Sensitivity, Sp: Specificity, AUC: Area Curve, Pr: Precision.  
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**\#Groups** & \
\begin{tabular}{l} **\#Features** \\ **(Mean)** \\ \end{tabular} & **Acc (Mean)** & **Sn (Mean)** & **Sp (Mean)** &
\begin{tabular}{l} **F-Measure** \\ **(Mean)** \\ \end{tabular} & **AUC (Mean)** & **Pr (Mean)** \\ \hline
10 & 1026.75 & 0.88 & 0.69 & 0.96 & 0.77 & 0.95 & 0.91 \\
9 & 795.75 & 0.88 & 0.72 & 0.96 & 0.79 & 0.95 & 0.90 \\
8 & 657.26 & 0.87 & 0.70 & 0.95 & 0.77 & 0.95 & 0.89 \\
7  
We re-ran our method by removing this physico-chemical group from the grouping to demonstrate how important physico-chemical properties are in antimicrobial peptide prediction. As seen in Table 4, when the physico-chemical properties are extracted, on the Gram-negative dataset, a single group generated by AMP-GSM includes 38.27 features (averaged over 100-fold MCCV iterations), and this group achieves an accuracy of only 87% and an AUC value of 93%. However, when physico-chemical properties are included, this rate was 95% for accuracy and 99% for AUC (as shown in Table 2). Likewise, for the Gram-positive dataset of Dataset 1, when the physico-chemical properties are removed, 82% accuracy and 90% AUC were obtained (shown in Table 5) by using a single group, including 37.67 features (averaged over 100-fold MCCV iterations), while 92% accuracy and 98% AUC values were obtained when physico-chemical properties were included in the analysis (shown in Table 3).