ArticleBioinformatics (Oxford, England)2013
An image-based multi-label human protein subcellular localization predictor (iLocator) reveals protein mislocalizations in cancer tissues.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 70 citations in OpenAlex.
- ProteinFormer: protein subcellular localization based on bioimages and modified pre-trained transformer.BMC genomics · 2025Article
- Differentially localized protein identification for breast cancer based on deep learning in immunohistochemical images.Communications biology · 2024Article
- Dual-Signal Feature Spaces Map Protein Subcellular Locations Based on Immunohistochemistry Image and Protein Sequence.Sensors (Basel, Switzerland) · 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
- 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
- Automated classification of protein expression levels in immunohistochemistry images to improve the detection of cancer biomarkers.BMC bioinformatics · 2022Article
- Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells.International journal of molecular sciences · 2022Article
- A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data.PLoS computational biology · 2022Article
- PScL-DDCFPred: an ensemble deep learning-based approach for characterizing multiclass subcellular localization of human proteins from bioimage data.Bioinformatics (Oxford, England) · 2022Article
- Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging.Frontiers in genetics · 2022Article
- Gm-PLoc: A Subcellular Localization Model of Multi-Label Protein Based on GAN and DeepFM.Frontiers in genetics · 2022Article
- PScL-HDeep: image-based prediction of protein subcellular location in human tissue using ensemble learning of handcrafted and deep learned features with two-layer feature selection.Briefings in bioinformatics · 2021Article
- Article
- Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer.BMC bioinformatics · 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
- Bioimaging-based detection of mislocalized proteins in human cancers by semi-supervised learning.Bioinformatics (Oxford, England) · 2015Article
- Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification?TheScientificWorldJournal · 2014Article
- A Self-Training Subspace Clustering Algorithm under Low-Rank Representation for Cancer Classification on Gene Expression Data.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
Abstract
motivationHuman cells are organized into compartments of different biochemical cellular processes. Having proteins appear at the right time to the correct locations in the cellular compartments is required to conduct their functions in normal cells, whereas mislocalization of proteins can result in pathological diseases, including cancer.
resultsTo reveal the cancer-related protein mislocalizations, we developed an image-based multi-label subcellular location predictor, iLocator, which covers seven cellular localizations. The iLocator incorporates both global and local image descriptors and generates predictions by using an ensemble multi-label classifier. The algorithm has the ability to treat both single- and multiple-location proteins. We first trained and tested iLocator on 3240 normal human tissue images that have known subcellular location information from the human protein atlas. The iLocator was then used to generate protein localization predictions for 3696 protein images from seven cancer tissues that have no location annotations in the human protein atlas. By comparing the output data from normal and cancer tissues, we detected eight potential cancer biomarker proteins that have significant localization differences with P-value < 0.01. AVAILABILITY: http://www.csbio.sjtu.edu.cn/bioinf/iLocator/
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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.