NeuroSeq: An Integrated Learning Framework for Enhanced Neuropeptide Prediction and Therapeutic Discovery

Neuropeptides, crucial in neural signaling and present throughout the nervous system, are integral to processes like stress response and neural repair.Traditional prediction methods fall short due to their complexity, necessitating advanced strategies.Our study introduces NeuroSeq, an integrated learning network that improves neuropeptides' prediction accuracy and efficiency.It employs pre-trained convolutional networks, advanced CNNs, and TCNs to learn complex amino acid patterns automatically.Combining machine and deep learning via average probability voting, an ensemble learning method further enhances predictions.We also propose a technique for optimizing neuropeptide sequences and assessing their reliability, generating high-confidence candidates.Experiments show NeuroSeq surpasses existing models, hastening neuropeptide identification and progressing peptide-based neurotherapeutics.This research emphasizes deep learning's potential in neuropeptide studies, advancing biological insights and therapeutic development.Neu-roSeq marks a significant advance in computationally unraveling neuropeptide intricacies, benefiting neuroscience and clinical applications.