# AMP-GSM: Prediction of Antimicrobial Peptides via a Grouping-Scoring-Modeling Approach
###### Abstract
They also tested the effect of different feature selection techniques. According to their findings, RF produced the best outcome, with over 92% accuracy on all tested organisms. [PERSON] et al. designed a model called IAMPE for the prediction of AMPs [16]. In this model, using the AMP datasets obtained from different databases, such as CAMP [3], LAMP [5], and AntiBP [10], the classification system was built using naive Bayes (NB), k-NN, SVM, RF, and extreme gradient boosting (XGBoost) classifiers based on the composition and physico-chemical characteristics of peptides. On the benchmark dataset, the combined features achieved 95% accuracy. [PERSON] et al. [17] reported a comprehensive study based on ML methods to predict AMPs. Using five-fold cross-validation, they evaluated six widely used feature selection algorithms and eleven traditional machine learning techniques. They found that SVM, RF, and XGBoost were outstanding machine learning techniques for AMP prediction based on a number of parameters. In contrast to the typical ML techniques that need pre-existing domain expertise and cleverly designed input features, deep neural networks (DNNs) have been employed in numerous bioinformatics tasks and they can automatically learn high-level characteristics. Recently, deep learning techniques have also been applied to solve the antimicrobial peptides prediction problem [18; 19; 20]. [PERSON] et al. designed a model called Deep-AntiFP for the prediction of antifungal peptides [21]. This model includes three dense layers and uses composite physico-chemical properties (CPP), a quasi-sequence order, and a reduced amino acid alphabet as input features. They obtained 89.08% accuracy with their DNN model using an independent dataset. [PERSON] proposed a model with three different sequence encodings and two image-based DNNs (RESNET-50 and VGG-16) to improve prediction accuracy of short AMPs [22]. Compared with RESNET-50, which had 96.14% training accuracy and 83.87% testing accuracy, VGG-16 produced more accurate results, with 98.30% training accuracy and 87.37% testing accuracy for predicting short AMPs. For the purpose of identifying AMPs, [PERSON] et al. created a deep neural network that included an embedding layer and many convolutional layers [23]. Their model outperformed the current models in terms of accuracy (92%) [23]. The deep CNN approach with one-hot encoding was suggested by [PERSON] et al. to produce the input features for AMP identification. They demonstrated that CNN outperformed simple RNN, long short-term memory (LSTM), LSTM with a gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) [24]. [PERSON] et al. built a model called HydrAMP which is a conditional variational autoencoder [25]. This model learns a lower-dimensional, continuous chemical space containing peptides and maintains their antimicrobial characteristics [25]. Most of the machine learning models utilized for AMP prediction are based on the physico-chemical properties of antimicrobial peptides, such as net charge, isoelectric point, hydrophobic moment, penetration depth, tilt angle, etc. [8; 26]. Apart from physico-chemical properties, there are also approaches that include sequence-based features, including amino acid composition, dipeptide composition, tripeptide composition, etc. [3; 17; 27], [28]. Additionally, there are studies that involve structure-based, linguistic-based features [29; 30; 31]. In the present study, for the antimicrobial peptide prediction task, we aim to develop a new computational approach that incorporates different types of AMP features and takes advantage of the characteristics of these groups. We attempt to show that one can increase the antimicrobial peptide prediction performance by using the selected groups of features that are identified with the proposed AMP-GSM method.