ReviewMolecular therapy. Nucleic acids2025
Computational approaches for identifying neuropeptides: A comprehensive review.
Review in Molecular therapy. Nucleic acids, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Insect neuropeptides as agents for pest control: potential and challenges.Biological research · 2025Review
- Decoding Neuropeptide Complexity: Advancing Neurobiological Insights from Invertebrates to Vertebrates through Evolutionary Perspectives.ACS chemical neuroscience · 2025Review
- MFP-MFL: Leveraging Graph Attention and Multi-Feature Integration for Superior Multifunctional Bioactive Peptide Prediction.International journal of molecular sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuropeptides (NPs) are key signaling molecules that interact with G protein-coupled receptors, influencing neuronal activities and developmental pathways, as well as the endocrine and immune systems. They are significant in disease contexts, offering potential therapeutic targets for conditions such as anxiety, neurological disorders, cardiovascular health, and diabetes. Understanding and detecting NPs is crucial because of their complex functions in health and disease. Historically, identifying NPs via wet lab techniques has been time-consuming and costly. However, integrating computational methods has shown the potential to improve efficiency, accuracy, and cost-effectiveness. Computational techniques, such as artificial intelligence and machine learning, have been extensively researched in recent years for the identification of NP. This review explores the application of machine learning (ML) techniques in predicting various aspects of NPs, including their sequences, cleavage sites, and precursors. Additionally, it provides insights into databases containing NP metadata and specialized tools used in this domain.
Indexed as
Identifiers
What Socratic 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 Socratic graph.