NeuroPpred-Fuse: an interpretable stacking model for prediction of neuropeptides by fusing sequence information and feature selection methods

Abstract

Neuropeptides acting as signaling molecules in the nervous system of various animals play crucial roles in a wide range of physiological functions and hormone regulation behaviors. Neuropeptides offer many opportunities for the discovery of new drugs and targets for the treatment of neurological diseases. In recent years, there have been several data-driven computational predictors of various types of bioactive peptides, but the relevant work about neuropeptides is little at present. In this work, we developed an interpretable stacking model, named NeuroPpred-Fuse, for the prediction of neuropeptides through fusing a variety of sequence-derived features and feature selection methods. Specifically, we used six types of sequence-derived features to encode the peptide sequences and then combined them. In the first layer, we ensembled three base classifiers and four feature selection algorithms, which select non-redundant important features complementarily. In the second layer, the output of the first layer was merged and fed into logistic regression (LR) classifier to train the model. Moreover, we analyzed the selected features and explained the feasibility of the selected features. Experimental results show that our model achieved 90.6% accuracy and 95.8% AUC on the independent test set, outperforming the state-of-the-art models. In addition, we exhibited the distribution of selected features by these tree models and compared the results on the training set to that on the test set. These results fully showed that our model has a certain generalization ability. Therefore, we expect that our model would provide important advances in the discovery of neuropeptides as new drugs for the treatment of neurological diseases.

NeuroPpred-SVM: A New Model for Predicting Neuropeptides Based on Embeddings of BERT

“…AAC has been used as features for building many different models, ,,, which consists of the relative abundances of the 20 types of natural amino acids in a specified peptide segment. Given a kind of residue, R i represents the occurrent frequency of the residue; its relative abundance can be calculated as follows f i = R i N , goodbreak0em2em⁣ ( i = 1 , 2 , 3 , ... , 20 ) where N refers to the length of a specified peptide; thus, we can get the AAC feature vector of the peptide segment as F normalA normalA normalC = ( f 1 , f 2 , f 3 , ... , f 20 ) …”

Prediction of anti-inflammatory peptides by a sequence-based stacking ensemble model named AIPStack

“…Many previous studies have shown that the ensemble model can achieve better predictive performance than single models in the ensemble, and reduce the generalization error of the prediction ( Charoenkwan et al., 2021 ; Mishra et al., 2019 ; Basith et al., 2022 ; Liang et al., 2021 ; Jiang et al., 2021 ; Guo et al., 2021 ). The existing ensemble learning strategies include boosting, bagging, and stacking ( Verma and Mehta, 2017 ).…”

“…Second, three-quarters of existing methods only applied a single algorithm. However, lots of studies have proven that the ensemble learning model usually outperforms the single-algorithm-based model ( Guo et al., 2021 ; Jiang et al., 2021 ; Basith et al., 2022 ; Liang et al., 2021 ; Mishra et al., 2019 ). Thus, the utilization of an ensemble learning strategy might improve the performance of AIP identification.…”

Method for predicting the remaining mileage of electric vehicles based on dimension expansion and model fusion

“…In addition, we compared the proposed method with previously published methods such as PredT4SE-Stack and NeuroPpred-Fuse [55,56]. Therefore, our approach was only compared them from the structural aspect.…”