ArticleBMC bioinformatics2020
Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 34 citations in OpenAlex.
- An effective study on the diagnosis of colon cancer with the developed local binary pattern method.Scientific reports · 2025Article
- Statistical analysis supports pervasive RNA subcellular localization and alternative 3' UTR regulation.eLife · 2024Article
- A Review for Artificial Intelligence Based Protein Subcellular Localization.Biomolecules · 2024Review
- Dual-Signal Feature Spaces Map Protein Subcellular Locations Based on Immunohistochemistry Image and Protein Sequence.Sensors (Basel, Switzerland) · 2023Article
- Article
- PScL-2LSAESM: bioimage-based prediction of protein subcellular localization by integrating heterogeneous features with the two-level SAE-SM and mean ensemble method.Bioinformatics (Oxford, England) · 2023Article
- HAR_Locator: a novel protein subcellular location prediction model of immunohistochemistry images based on hybrid attention modules and residual units.Frontiers in molecular biosciences · 2023Article
- Automated classification of protein expression levels in immunohistochemistry images to improve the detection of cancer biomarkers.BMC bioinformatics · 2022Article
- Applications of Neural Networks in Biomedical Data Analysis.Biomedicines · 2022Review
- Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics.Frontiers in bioinformatics · 2022Review
- Deep Learning Approaches for Automatic Localization in Medical Images.Computational intelligence and neuroscience · 2022Review
- Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble.Computational and mathematical methods in medicine · 2021Article
- Bird Eye View of Protein Subcellular Localization Prediction.Life (Basel, Switzerland) · 2020Review
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundProtein biomarkers play important roles in cancer diagnosis. Many efforts have been made on measuring abnormal expression intensity in biological samples to identity cancer types and stages. However, the change of subcellular location of proteins, which is also critical for understanding and detecting diseases, has been rarely studied.
resultsIn this work, we developed a machine learning model to classify protein subcellular locations based on immunohistochemistry images of human colon tissues, and validated the ability of the model to detect subcellular location changes of biomarker proteins related to colon cancer. The model uses representative image patches as inputs, and integrates feature engineering and deep learning methods. It achieves 92.69% accuracy in classification of new proteins. Two validation datasets of colon cancer biomarkers derived from published literatures and the human protein atlas database respectively are employed. It turns out that 81.82 and 65.66% of the biomarker proteins can be identified to change locations.
conclusionsOur results demonstrate that using image patches and combining predefined and deep features can improve the performance of protein subcellular localization, and our model can effectively detect biomarkers based on protein subcellular translocations. This study is anticipated to be useful in annotating unknown subcellular localization for proteins and discovering new potential location biomarkers.
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