Integrating spatial gene expression and breast tumour morphology via deep learning
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Integrating spatial gene expression and breast tumour morphology via deep learning
Authors
Keywords
-
Journal
Nature Biomedical Engineering
Volume 4, Issue 8, Pages 827-834
Publisher
Springer Science and Business Media LLC
Online
2020-06-23
DOI
10.1038/s41551-020-0578-x
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+
- (2019) Chee-Huat Linus Eng et al. NATURE
- Modeling Spatial Correlation of Transcripts with Application to Developing Pancreas
- (2019) Ruishan Liu et al. Scientific Reports
- 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
- (2018) Geert Litjens et al. GigaScience
- Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images
- (2018) Pegah Khosravi et al. EBioMedicine
- Clinically applicable deep learning for diagnosis and referral in retinal disease
- (2018) Jeffrey De Fauw et al. NATURE MEDICINE
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
- (2018) Nicolas Coudray et al. NATURE MEDICINE
- Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections
- (2018) Fredrik Salmén et al. Nature Protocols
- Dermatologist-level classification of skin cancer with deep neural networks
- (2017) Andre Esteva et al. NATURE
- Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies
- (2016) Elior Rahmani et al. NATURE METHODS
- Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
- (2016) P. L. Stahl et al. SCIENCE
- Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
- (2016) Kun-Hsing Yu et al. Nature Communications
- ImageNet Large Scale Visual Recognition Challenge
- (2015) Olga Russakovsky et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Spatially resolved, highly multiplexed RNA profiling in single cells
- (2015) K. H. Chen et al. SCIENCE
- Highly Multiplexed Subcellular RNA Sequencing in Situ
- (2014) J. H. Lee et al. SCIENCE
- Biglycan is a specific marker and an autocrine angiogenic factor of tumour endothelial cells
- (2012) K Yamamoto et al. BRITISH JOURNAL OF CANCER
- Identification of Genomic Targets of Transcription Factor Aebp1 and its role in Survival of Glioma Cells
- (2012) J. Ladha et al. MOLECULAR CANCER RESEARCH
- Comprehensive molecular portraits of human breast tumours
- (2012) Daniel C. Koboldt et al. NATURE
- Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
- (2012) Marco Gerlinger et al. NEW ENGLAND JOURNAL OF MEDICINE
- Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software
- (2011) Lee Kamentsky et al. BIOINFORMATICS
- Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
- (2009) Da Wei Huang et al. Nature Protocols
- Macrophage-Derived SPARC Bridges Tumor Cell-Extracellular Matrix Interactions toward Metastasis
- (2008) Sabina Sangaletti et al. CANCER RESEARCH
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now