ArticleBMC bioinformatics2022
Automated classification of protein expression levels in immunohistochemistry images to improve the detection of cancer biomarkers.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Glycoprotein Mucin 13 Expression as a Theranostic Target in Colorectal Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images.Medical & biological engineering & computing · 2024Article
- An overview of the detection of bovine respiratory disease complex pathogens using immunohistochemistry: emerging trends and opportunities.Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc · 2024Review
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Authors and funding
5 authors at 3 institutions in 1 country.
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Abstract
backgroundThe expression changes of some proteins are associated with cancer progression, and can be used as biomarkers in cancer diagnosis. Automated systems have been frequently applied in the large-scale detection of protein biomarkers and have provided a valuable complement for wet-laboratory experiments. For example, our previous work used an immunohistochemical image-based machine learning classifier of protein subcellular locations to screen biomarker proteins that change locations in colon cancer tissues. The tool could recognize the location of biomarkers but did not consider the effect of protein expression level changes on the screening process.
resultsIn this study, we built an automated classification model that recognizes protein expression levels in immunohistochemical images, and used the protein expression levels in combination with subcellular locations to screen cancer biomarkers. To minimize the effect of non-informative sections on the immunohistochemical images, we employed the representative image patches as input and applied a Wasserstein distance method to determine the number of patches. For the patches and the whole images, we compared the ability of color features, characteristic curve features, and deep convolutional neural network features to distinguish different levels of protein expression and employed deep learning and conventional classification models. Experimental results showed that the best classifier can achieve an accuracy of 73.72% and an F1-score of 0.6343. In the screening of protein biomarkers, the detection accuracy improved from 63.64 to 95.45% upon the incorporation of the protein expression changes.
conclusionsMachine learning can distinguish different protein expression levels and speed up their annotation in the future. Combining information on the expression patterns and subcellular locations of protein can improve the accuracy of automatic cancer biomarker screening. This work could be useful in discovering new cancer biomarkers for clinical diagnosis and research.
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