ArticleHeliyon2024
MLAFP-XN: Leveraging neural network model for development of antifungal peptide identification tool.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Enhancing the identification of malonylation sites using AlphaFold2 and ensemble learning.Molecular diversity · 2026Article
- CKAN-ATHP: a predictor for antihypertensive peptides based on sample augmentation and loss improvement strategies using the convolutional Kolmogorov-Arnold network.Journal of cheminformatics · 2026Article
- Atrial fibrillation identification in patients with non-alcoholic fatty liver disease: a machine learning model based on immune-inflammatory markers.Diabetology & metabolic syndrome · 2025Article
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Next-generation antifungal peptide discovery: the synergy of artificial intelligence and omics technologies.World journal of microbiology & biotechnology · 2025Review
- Refining cancer prediction with DNA sequencing and combined machine learning approaches.Scientific reports · 2025Article
- AAindexNC: Estimating the Physicochemical Properties of Non-Canonical Amino Acids, Including Those Derived from the PDB and PDBeChem Databank.International journal of molecular sciences · 2024Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infectious fungi have been an increasing global concern in the present era. A promising approach to tackle this pressing concern involves utilizing Antifungal peptides (AFP) to develop an antifungal drug that can selectively eliminate fungal pathogens from a host with minimal toxicity to the host. Accordingly, identifying precise therapeutic antifungal peptides is crucial for developing effective drugs and treatments. This study proposed MLAFP-XN, a neural network-based strategy for accurately detecting active AFP in sequencing data to achieve this objective. In this work, eight feature extraction techniques and the XGB feature selection strategy are utilized together to present an enhanced methodology. A total of 24 classification models were evaluated, and the most effective four have been selected. Each of these models demonstrated superior accuracy on independent test sets, with respective scores of 97.93 %, 99.47 %, and 99.48 %. Our model outperforms current state of the art methods. In addition, we created a companion website to demonstrate our AFP recognition process and use SHAP to identify the most influential properties.
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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.