# Boosted Prediction of Antihypertensive Peptides Using Deep Learning
## 1 Introduction
The dataset was divided into four different categories to extract features. The categories were named (1) tiny (dipeptides and tripeptides), (2) small (tetrapeptides, pentapeptides, and hexapeptides), (3) medium (i.e., sizes ranging between seven and twelve), and (4) large peptides (greater than twelve amino acids). For tiny peptides, chemical descriptors were extracted to generate support vector machine (SVM)-based regression models, and they achieved a correlation of 0.701 for dipeptides and 0.543 for tripeptides. For smaller peptides, SVM-based classification models were developed, and the maximum obtained accuracies were 76.67% for tetrapeptide, 72.04% for pentapeptide, and 77.39% for hexapeptides. For medium and large peptides, amino acid composition features were extracted to develop SVM-based classification models, and accuracies of 82.61% for medium peptides and 84.21% for large peptides were attained. Moreover, a web-based platform called AHTpin was also created to screen, predict, and design AHTPs. [PERSON] et al. developed a computerized AHTPs prediction system [10] that employs random forest (RF) models. The models are trained by using the groupings of amino acid composition, dipeptide composition, and pseudo amino acid composition feature encoding techniques. The system demonstrates a marginal improvement over AHTpin, with an accuracy of 84.73% when run on an independent test dataset. Furthermore, the feature importance analysis emphasized the preference at the C-terminal of the proline amino acids and non-polar amino acids, and also the capacity of little peptides for vigorous activity. An online web platform (called PAAP) is also available for public use of the proposed model it contains. The mAHTpred meta-predictor is another approach to classifying AHITPs [11]. To identify AHITPs, [PERSON] et al. developed mAHTpred by using eight feature encoding schemes to construct 51 feature vectors from two different datasets (benchmarking dataset and independent dataset). Extremely randomized tree (ERT)-based models were created by using these 51 feature vectors. A new feature vector consisting of the predicted probabilities of AHITPs was calculated by using the above mentioned ERT-based models. The new feature vector was then utilized as an input for four different ML algorithms: SVM, gradient boosting (GB), RF, and ERT. Final predictions were made by using an ensemble of SVM, GB, RF, and ERT models. AHTP prediction has been realized in [12], using recursive feature elimination. This work generates and feeds optimal features into the ensemble of four classification algorithms (SVM, C4.5 Decision Tree, random forest (RF), and extreme gradient boosting (XGBoost)) in order to achieve the final prediction. Deep-AmPEP30 is a recent deep learning-based model, devised for the prediction of short-length antimicrobial peptides (AMP), which is another important bioactive peptide sequence [13]. Deep-AmPEP30 employs CNN on a reduced set of amino acid composition (AAC) features. This group experimented with a benchmark dataset consisting of balanced classes, with accuracy scores of 77% and 85% gained by both Area under the Receiver Operating Characteristic Curve (AUC-ROC) and Area under the Precision-Recall Curve (AUC-PR), respectively. In this research activity, a deep learning-based antihypertensive peptide predictor is presented. The proposed approach uses the standard datasets used in [9] and [11]. Features are generated by using two feature-encoding techniques: amino acid composition (AAC) and g-gap dipeptide composition (g-gap DPC). Dipeptide composition features are further represented as red, green, and blue (RGB) images. An RGB image is created against each dataset. In the literature, ensemble-based methods have been successfully applied for the task of classification [11; 12]. In such methods, multiple classifiers are combined in parallel or in sequence to boost the classification performance.