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Updated: Sep 13, 2025

Multi-Faceted Mass Spectrometric Investigation of Neuropeptides in Callinectes sapidus

Multi-Faceted Mass Spectrometric Investigation of Neuropeptides in Callinectes sapidus

Published on: May 31, 2022

EnsembleNPPred: A Robust Approach to Neuropeptide Prediction and Recognition Using Ensemble Machine Learning and Deep

Supatcha Lertampaiporn¹, Warin Wattanapornprom², Chinae Thammarongtham¹

¹Biochemical Engineering and Systems Biology Research Group, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency at King Mongkut's University of Technology Thonburi, Bangkok 10150, Thailand.

Life (Basel, Switzerland)

|July 29, 2025

Summary

EnsembleNPPred, a novel computational tool, accurately identifies neuropeptide candidates using machine and deep learning. This framework aids researchers in prioritizing sequences for experimental validation, accelerating neuropeptide discovery.

Keywords:

bioinformatics combining methods computational peptide discovery deep learning ensemble learning machine learning neuropeptide prediction prediction model voting

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Last Updated: Sep 13, 2025

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Neuroscience
Bioinformatics
Computational Biology

Background:

Neuropeptides (NPs) are crucial signaling molecules regulating vital physiological functions.
Experimental identification of neuropeptides is laborious and costly.
Computational tools offer a cost-effective strategy for prioritizing NP candidates.

Purpose of the Study:

To develop an advanced computational framework for accurate neuropeptide identification.
To integrate traditional machine learning (ML) with deep learning (DL) for enhanced predictive performance.
To provide a tool for prioritizing candidate neuropeptide sequences for experimental validation.

Main Methods:

Developed EnsembleNPPred, an ensemble learning framework combining ML and DL models.
Utilized Support Vector Machine (SVM), Extra Trees (ET), and a CNN-based DL model.
Employed a majority voting mechanism to aggregate predictions from individual classifiers.

Main Results:

EnsembleNPPred demonstrated superior accuracy and improved sensitivity-specificity balance over existing methods.
Achieved an average accuracy of 91.92% on diverse neuropeptide families from the NeuroPep database.
Showcased strong generalization capabilities across different neuropeptide classes.

Conclusions:

EnsembleNPPred is a robust and accurate tool for computational neuropeptide identification.
The framework effectively supports early-stage neuropeptide discovery and experimental validation.
This approach accelerates research in neuropeptide function and therapeutic development.