Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer

[1]  Isabelle Bichindaritz,et al.  Bioimage-Based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[2]  Hong-Bin Shen,et al.  ImPLoc: a multi-instance deep learning model for the prediction of protein subcellular localization based on immunohistochemistry images , 2019, Bioinform..

[3]  Hao Xu,et al.  Analysis of the Human Protein Atlas Image Classification competition , 2019, Nature Methods.

[4]  Yang Liu,et al.  MIC_Locator: a novel image-based protein subcellular location multi-label prediction model based on multi-scale monogenic signal representation and intensity encoding strategy , 2019, BMC Bioinformatics.

[5]  Dietrich Rebholz-Schuhmann,et al.  Predicting links between tumor samples and genes using 2-Layered graph based diffusion approach , 2019, BMC Bioinformatics.

[6]  Shunfang Wang,et al.  An Improved Process for Generating Uniform PSSMs and Its Application in Protein Subcellular Localization via Various Global Dimension Reduction Techniques , 2019, IEEE Access.

[7]  Jijun Tang,et al.  Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou's general PseAAC. , 2019, Journal of theoretical biology.

[8]  Y. Wang,et al.  DBS: a fast and informative segmentation algorithm for DNA copy number analysis , 2019, BMC Bioinformatics.

[9]  Xing-Ming Zhao,et al.  DeepPhos: prediction of protein phosphorylation sites with deep learning , 2019, Bioinform..

[10]  Sepp Hochreiter,et al.  Human-level Protein Localization with Convolutional Neural Networks , 2018, ICLR.

[11]  Casper F Winsnes,et al.  Deep learning is combined with massive-scale citizen science to improve large-scale image classification , 2018, Nature Biotechnology.

[12]  Yu Liu,et al.  PTM-ssMP: A Web Server for Predicting Different Types of Post-translational Modification Sites Using Novel Site-specific Modification Profile , 2018, International journal of biological sciences.

[13]  Hong-Bin Shen,et al.  Bioimage-based protein subcellular location prediction: a comprehensive review , 2018, Frontiers of Computer Science.

[14]  Ehsan Kazemi,et al.  Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images , 2017, bioRxiv.

[15]  Ao Li,et al.  Prediction of post-translational modification sites using multiple kernel support vector machine , 2017, PeerJ.

[16]  Yolanda T. Chong,et al.  Automated analysis of high‐content microscopy data with deep learning , 2017, Molecular systems biology.

[17]  Hong-Bin Shen,et al.  Hum‐mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features , 2016, Bioinform..

[18]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Ying Ju,et al.  Human Protein Subcellular Localization with Integrated Source and Multi-label Ensemble Classifier , 2016, Scientific Reports.

[20]  Leopold Parts,et al.  Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning , 2016, G3: Genes, Genomes, Genetics.

[21]  Yili Yang,et al.  Loss of nuclear localization of TET2 in colorectal cancer , 2016, Clinical Epigenetics.

[22]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Daoqiang Zhang,et al.  Human cell structure-driven model construction for predicting protein subcellular location from biological images , 2015, Bioinform..

[25]  R. Murphy,et al.  Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers , 2014, Proceedings of the National Academy of Sciences.

[26]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  M. Schuldiner,et al.  The emergence of proteome-wide technologies: systematic analysis of proteins comes of age , 2014, Nature Reviews Molecular Cell Biology.

[28]  Yang Zhang,et al.  An image-based multi-label human protein subcellular localization predictor (iLocator) reveals protein mislocalizations in cancer tissues , 2013, Bioinform..

[29]  Chi-Ying F. Huang,et al.  Aberrant nuclear localization of EBP50 promotes colorectal carcinogenesis in xenotransplanted mice by modulating TCF-1 and β-catenin interactions. , 2012, The Journal of clinical investigation.

[30]  Wolfgang Link,et al.  Protein localization in disease and therapy , 2011, Journal of Cell Science.

[31]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[32]  E. Lundberg,et al.  Towards a knowledge-based Human Protein Atlas , 2010, Nature Biotechnology.

[33]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[34]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  Robert F. Murphy,et al.  Automated comparison of protein subcellular location patterns between images of normal and cancerous tissues , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[36]  R. Murphy,et al.  A framework for the automated analysis of subcellular patterns in human protein atlas images. , 2008, Journal of proteome research.

[37]  G. Zhou,et al.  Protein Expression Profiling of Breast Cancer Cells by Dissociable Antibody Microarray (DAMA) Staining*S , 2008, Molecular & Cellular Proteomics.

[38]  R. Mangues,et al.  Celecoxib induces anoikis in human colon carcinoma cells associated with the deregulation of focal adhesions and nuclear translocation of p130Cas , 2006, International journal of cancer.

[39]  Kai Huang,et al.  Feature reduction for improved recognition of subcellular location patterns in fluorescence microscope images , 2003, SPIE BiOS.

[40]  D. Rimm,et al.  Tissue Microarray Analysis of β-Catenin in Colorectal Cancer Shows Nuclear Phospho-β-catenin Is Associated with a Better Prognosis , 2001 .

[41]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[42]  D L Rimm,et al.  Tissue microarray analysis of beta-catenin in colorectal cancer shows nuclear phospho-beta-catenin is associated with a better prognosis. , 2001, Clinical cancer research : an official journal of the American Association for Cancer Research.