ArticleBMC genomics2025
ProteinFormer: protein subcellular localization based on bioimages and modified pre-trained transformer.
Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- A lightweight deep learning model with channel attention for kidney cell classification from microscopy images.Scientific reports · 2026Article
- The good, the bad, and the ugly: opportunities, challenges, and pitfalls in spatial proteomics modeling.Briefings in bioinformatics · 2026Review
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7 authors.
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Abstract
backgroundTraditional biological experiments for protein subcellular localization are costly and inefficient, while sequence-based methods fail to capture spatial dynamics of protein translocation. Existing deep learning models primarily rely on convolutions and lack global image integration, particularly in small-sample scenarios.
methodsWe propose ProteinFormer, a novel model integrating biological images with an enhanced pre-trained transformer architecture. It combines ResNet for local feature extraction and a modified transformer for global information fusion. To address data scarcity, we further develop GL-ProteinFormer, which incorporates residual learning, inductive bias, and a ConvFFN.
resultsProteinFormer achieves state-of-the-art performance on the Cyto_2017 dataset for both single-label (91% [Formula: see text]-score) and multi-label (81% [Formula: see text]-score) tasks. GL-ProteinFormer demonstrates superior generalization on the limited-sample IHC_2021 dataset (81% [Formula: see text]-score), with ConvFFN improving Accuracy by 4% while reducing computational costs.
conclusionProteinFormer and its GL-ProteinFormer variant show superior performance over existing convolution-based methods. By fusing biological images with transformer-based global feature modeling, the proposed approach offers a robust and efficient solution for protein subcellular localization, especially in data-limited settings.
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