ArticleBioinformatics (Oxford, England)2020
Learning complex subcellular distribution patterns of proteins via analysis of immunohistochemistry images.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Biomarkers for Prostate Cancer Aggressiveness in Puerto Rican Men: Analysis of Phospho-Rb S249, N-cadherin, β-catenin, and E-cadherin Expression in Prostate Biopsies.Ponce Health Sciences University scientific journal · 2025Article
- Protein Classes Predicted by Molecular Surface Chemical Features: Machine Learning-Assisted Classification of Cytosol and Secreted Proteins.The journal of physical chemistry. B · 2024Article
- 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
- Gm-PLoc: A Subcellular Localization Model of Multi-Label Protein Based on GAN and DeepFM.Frontiers in genetics · 2022Article
- Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics.Frontiers in bioinformatics · 2022Review
- Article
- Bird Eye View of Protein Subcellular Localization Prediction.Life (Basel, Switzerland) · 2020Review
- 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
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Authors and funding
3 authors.
Funding
Abstract
motivationSystematic and comprehensive analysis of protein subcellular location as a critical part of proteomics ('location proteomics') has been studied for many years, but annotating protein subcellular locations and understanding variation of the location patterns across various cell types and states is still challenging.
resultsIn this work, we used immunohistochemistry images from the Human Protein Atlas as the source of subcellular location information, and built classification models for the complex protein spatial distribution in normal and cancerous tissues. The models can automatically estimate the fractions of protein in different subcellular locations, and can help to quantify the changes of protein distribution from normal to cancer tissues. In addition, we examined the extent to which different annotated protein pathways and complexes showed similarity in the locations of their member proteins, and then predicted new potential proteins for these networks. AVAILABILITY AND IMPLEMENTATION: The dataset and code are available at: www.csbio.sjtu.edu.cn/bioinf/complexsubcellularpatterns. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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