ArticleNature communications2025
Enhancing peptide identification in metaproteomics through curriculum learning in deep learning.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Metagenomic Tools as Predictive Approaches to Decipher Soil and Rhizosphere Microbial Communities and Their Functions for Sustainable Agriculture.International journal of genomics · 2026Review
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Authors and funding
8 authors.
Funding
Abstract
Metaproteomics offers a powerful window into the active functions of microbial communities, but accurately identifying peptides remains challenging due to the size and incompleteness of protein databases derived from metagenomes. These databases often contain vastly more sequences than those from single organisms, creating a computational bottleneck in peptide-spectrum match (PSM) filtering. Here we present WinnowNet, a deep learning-based method for PSM filtering, available in two versions: one using transformers and the other convolutional neural networks. Both variants are designed to handle the unordered nature of PSM data and are trained using a curriculum learning strategy that moves from simple to complex examples. WinnowNet consistently achieves more true identifications at equivalent false discovery rates compared to leading tools, including Percolator, MS
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Registered trials
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