Identifying Neuropeptides via Evolutionary and Sequential Based Multi-Perspective Descriptors by Incorporation With Ensemble Classification Strategy

Abstract

Neuropeptides (NPs) are a kind of neuromodulator/ neurotransmitter that works as signaling molecules in the central nervous system, and perform major roles in physiological and hormone regulation activities. Recently, machine learning-based therapeutic agents have gained the attention of researchers due to their high and reliable prediction results. However, the unsatisfactory performance of the existing predictors is due to their high execution cost and minimum predictive results. Therefore, the development of a reliable prediction is highly indispensable for scientists to effectively predict NPs. In this study, we presented an automatic and computationally effective model for identifying of NPs. The evolutionary information is formulated using a bigram position-specific scoring matrix (Bi-PSSM) and K-spaced bigram (KSB). Moreover, for noise reduction, a discrete wavelet transform (DWT) is utilized to form Bi-PSSM_DWT and KSB_DWT based high discriminative vectors. In addition, one-hot encoding is also employed to collect sequential features from peptide samples. Finally, a multi-perspective feature set of sequential and embedded evolutionary information is formed. The optimum features are chosen from the extracted features via Shapley Additive exPlanations (SHAP) by evaluating the contribution of the extracted features. The optimal features are trained via six classification models i.e., XGB, ETC, SVM, ADA, FKNN, and LGBM. The predicted labels of these learners are then provided to a genetic algorithm to form an ensemble classification approach. Hence, our model achieved a higher predictive accuracy of 94.47% and 92.55% using training sequences and independent sequences, respectively. Which is ∼3% highest predictive accuracy than present methods. It is suggested that our presented tool will be beneficial and may execute a substantial role in drug development and research academia. The source code and all datasets are publicly available at https://github.com/shahidawkum/Target-ensC_NP.

Classification of bioactive peptides: A systematic benchmark of models and encodings

“…To evaluate the practical effectiveness of current BP classification methods, CICERON was tested against state-of-the-art (SOTA) classifiers specific to the various peptide functional classes, which were selected upon careful literature inspection [62] , [63] , [64] , [65] , [66] , [67] . The main characteristics of SOTA models and their development dataset are summarized in Table 2 .…”

Optimizing classification of diseases through language model analysis of symptoms

“…In the future work, we further plan to use another recent predictors such as pAtbP-EnC 45 , AIPs-SnTCN 46 , AFP-CMBPred 47 , cACP-DeepGram 48 , iACP-GAEnsC 49 , and Target-ensC_NP. Furthermore, we intended to used the CD-HIT tool was utilized to eliminate redundant peptide samples with homology 50 .…”

AIPs-SnTCN: Predicting Anti-Inflammatory Peptides Using fastText and Transformer Encoder-Based Hybrid Word Embedding with Self-Normalized Temporal Convolutional Networks

“…96−98 A confusion matrix is initially generated to evaluate a model that captures true-positive, true-negative, false-positive, and false-negative outcomes in the training process. 99 While accuracy is commonly used as a reliable metric for evaluating classification models, it may not be sufficient when dealing with imbalanced training data sets. 72…”

“…In deep learning, various performance metrics are applied to assess the effectiveness of computational models across different aspects. − A confusion matrix is initially generated to evaluate a model that captures true-positive, true-negative, false-positive, and false-negative outcomes in the training process . While accuracy is commonly used as a reliable metric for evaluating classification models, it may not be sufficient when dealing with imbalanced training data sets. ,− Hence, we also incorporated additional performance assessment parameters such as sensitivity, specificity, Matthews’s correlation coefficient (MCC), and area under the curve (AUC) to evaluate our proposed model comprehensively. a c c u r a c y = 1 − A i p − + + A i p + − A i p + + A i p − s e n s i t i v i t y = 1 − A i p − + A i p + s p e c i f i c i t y =…”