ArticleProceedings of the National Academy of Sciences of the United States of America2014
Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer.Breast cancer research : BCR · 2016Trial
- Genome-wide identification of the crustacyanin gene family in Neocaridina denticulata sinensis based on comparative genomics and localization analysis.BMC genomics · 2025Article
- Differentially localized protein identification for breast cancer based on deep learning in immunohistochemical images.Communications biology · 2024Article
- A review from biological mapping to computation-based subcellular localization.Molecular therapy. Nucleic acids · 2023Review
- 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
- Parallel analysis of transcription, integration, and sequence of single HIV-1 proviruses.Cell · 2022Article
- The Studies of Prognostic Factors and the Genetic Polymorphism of Methylenetetrahydrofolate Reductase C667T in Thymic Epithelial Tumors.Frontiers in oncology · 2022Article
- Article
- Bird Eye View of Protein Subcellular Localization Prediction.Life (Basel, Switzerland) · 2020Review
- Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer.BMC bioinformatics · 2020Article
- Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images.Diagnostic pathology · 2020Article
- Learning complex subcellular distribution patterns of proteins via analysis of immunohistochemistry images.Bioinformatics (Oxford, England) · 2020Article
- MIC_Locator: a novel image-based protein subcellular location multi-label prediction model based on multi-scale monogenic signal representation and intensity encoding strategy.BMC bioinformatics · 2019Article
- A method for semi-automated image analysis of HLA class I tumour epithelium expression in rectal cancer.European journal of histochemistry : EJH · 2019Article
- Translational AI and Deep Learning in Diagnostic Pathology.Frontiers in medicine · 2019Review
- Quantitative imaging for development of companion diagnostics to drugs targeting HGF/MET.The journal of pathology. Clinical research · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Molecular biomarkers are changes measured in biological samples that reflect disease states. Such markers can help clinicians identify types of cancer or stages of progression, and they can guide in tailoring specific therapies. Many efforts to identify biomarkers consider genes that mutate between normal and cancerous tissues or changes in protein or RNA expression levels. Here we define location biomarkers, proteins that undergo changes in subcellular location that are indicative of disease. To discover such biomarkers, we have developed an automated pipeline to compare the subcellular location of proteins between two sets of immunohistochemistry images. We used the pipeline to compare images of healthy and tumor tissue from the Human Protein Atlas, ranking hundreds of proteins in breast, liver, prostate, and bladder based on how much their location was estimated to have changed. The performance of the system was evaluated by determining whether proteins previously known to change location in tumors were ranked highly. We present a number of candidate location biomarkers for each tissue, and identify biochemical pathways that are enriched in proteins that change location. The analysis technology is anticipated to be useful not only for discovering new location biomarkers but also for enabling automated analysis of biomarker distributions as an aid to determining diagnosis.
Indexed as
Identifiers
What OpenQuestion holds
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.