ArticleScientific reports2019
NeuroPIpred: a tool to predict, design and scan insect neuropeptides.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 58 citations in OpenAlex.
- NeuroPred-GMC: a dual-branch deep learning architecture for neuropeptide prediction based on gated dilated convolutional network and multi-scale convolutional network.Journal of computer-aided molecular design · 2026Article
- NeuroPpred-MSN: A Neuropeptide Prediction Model Based on Multi-feature Fusion and Siamese Networks.Interdisciplinary sciences, computational life sciences · 2026Article
- Insect neuropeptides as agents for pest control: potential and challenges.Biological research · 2025Review
- Toward high-efficiency, low-resource, and explainable neuropeptide prediction with MSKDNP.Briefings in bioinformatics · 2025Article
- EnsembleNPPred: A Robust Approach to Neuropeptide Prediction and Recognition Using Ensemble Machine Learning and Deep Learning Methods.Life (Basel, Switzerland) · 2025Article
- Insect Pest Control from Chemical to Biotechnological Approach: Constrains and Challenges.Insects · 2025Review
- Article
- Computational approaches for identifying neuropeptides: A comprehensive review.Molecular therapy. Nucleic acids · 2025Review
- Interpretable molecular encodings and representations for machine learning tasks.Computational and structural biotechnology journal · 2024Article
- iNP_ESM: Neuropeptide Identification Based on Evolutionary Scale Modeling and Unified Representation Embedding Features.International journal of molecular sciences · 2024Article
- A novel framework based on explainable AI and genetic algorithms for designing neurological medicines.Scientific reports · 2024Article
- Identification and expression profiling of neuropeptides and neuropeptide receptor genes in a natural enemy,Frontiers in physiology · 2024Article
- Article
- Unsupervised encoding selection through ensemble pruning for biomedical classification.BioData mining · 2023Article
- Recent advances in mass spectrometry analysis of neuropeptides.Mass spectrometry reviews · 2023Review
- NeuroCNN_GNB: an ensemble model to predict neuropeptides based on a convolution neural network and Gaussian naive Bayes.Frontiers in genetics · 2023Article
- CSM-peptides: A computational approach to rapid identification of therapeutic peptides.Protein science : a publication of the Protein Society · 2022Article
- Mass Spectrometry Approaches Empowering Neuropeptide Discovery and Therapeutics.Pharmacological reviews · 2022Review
- Propagation of seminal toxins through binary expression gene drives could suppress populations.Scientific reports · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Insect neuropeptides and their associated receptors have been one of the potential targets for the pest control. The present study describes in silico models developed using natural and modified insect neuropeptides for predicting and designing new neuropeptides. Amino acid composition analysis revealed the preference of residues C, D, E, F, G, N, S, and Y in insect neuropeptides The positional residue preference analysis show that in natural neuropeptides residues like A, N, F, D, P, S, and I are preferred at N terminus and residues like L, R, P, F, N, and G are preferred at C terminus. Prediction models were developed using input features like amino acid and dipeptide composition, binary profiles and implementing different machine learning techniques. Dipeptide composition based SVM model performed best among all the models. In case of NeuroPIpred_DS1, model achieved an accuracy of 86.50% accuracy and 0.73 MCC on training dataset and 83.71% accuracy and 0.67 MCC on validation dataset whereas in case of NeuroPIpred_DS2, model achieved 97.47% accuracy and 0.95 MCC on training dataset and 97.93% accuracy and 0.96 MCC on validation dataset. In order to assist researchers, we created standalone and user friendly web server NeuroPIpred, available at ( https://webs.iiitd.edu.in/raghava/neuropipred .).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.