ArticleBioinformatics (Oxford, England)2015
Bioimaging-based detection of mislocalized proteins in human cancers by semi-supervised learning.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- USSGAN: Unsupervised Spectral and Spatial Attention-Based Generative Adversarial Network for Cholangiocarcinoma Detection.Chemical & biomedical imaging · 2025Article
- Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images.Medical & biological engineering & computing · 2024Article
- A Review for Artificial Intelligence Based Protein Subcellular Localization.Biomolecules · 2024Review
- The expression and role of the Lem-D proteins Ankle2, Emerin, Lemd2, and TMPO in triple-negative breast cancer cell growth.Frontiers in oncology · 2024Article
- Automated classification of protein expression levels in immunohistochemistry images to improve the detection of cancer biomarkers.BMC bioinformatics · 2022Article
- Improving glomerular filtration rate estimation by semi-supervised learning: a development and external validation study.International urology and nephrology · 2021Article
- Protein Subcellular Localization Prediction.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Robust classification of cell cycle phase and biological feature extraction by image-based deep learning.Molecular biology of the cell · 2020Article
- A reference library for assigning protein subcellular localizations by image-based machine learning.The Journal of cell biology · 2020Article
- Article
- A semi-supervised machine learning framework for microRNA classification.Human genomics · 2019Article
- Multi-view Co-training for microRNA Prediction.Scientific reports · 2019Article
- Positive-unlabelled learning of glycosylation sites in the human proteome.BMC bioinformatics · 2019Article
- MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format.F1000Research · 2015Article
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Authors and funding
4 authors.
Funding
Abstract
motivationThere is a long-term interest in the challenging task of finding translocated and mislocated cancer biomarker proteins. Bioimages of subcellular protein distribution are new data sources which have attracted much attention in recent years because of their intuitive and detailed descriptions of protein distribution. However, automated methods in large-scale biomarker screening suffer significantly from the lack of subcellular location annotations for bioimages from cancer tissues. The transfer prediction idea of applying models trained on normal tissue proteins to predict the subcellular locations of cancerous ones is arbitrary because the protein distribution patterns may differ in normal and cancerous states.
resultsWe developed a new semi-supervised protocol that can use unlabeled cancer protein data in model construction by an iterative and incremental training strategy. Our approach enables us to selectively use the low-quality images in normal states to expand the training sample space and provides a general way for dealing with the small size of annotated images used together with large unannotated ones. Experiments demonstrate that the new semi-supervised protocol can result in improved accuracy and sensitivity of subcellular location difference detection. AVAILABILITY AND IMPLEMENTATION: The data and code are available at: www.csbio.sjtu.edu.cn/bioinf/SemiBiomarker/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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