Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors From Pathological Images and Multi-Omics Data

10 May 2023

Abstract excerpt

The tumor-infiltrating lymphocytes (TILs) and its correlation with tumors have shown significant values in the development of cancers. Many observations indicated that the combination of the whole-slide pathological images (WSIs) and genomic data can better characterize the immunological mechanisms of TILs. However, the existing image-genomic studies evaluated the TILs by the combination of pathological image and...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cancer Genomics and Diagnostics
Share to Cancer Immunotherapy and Biomarkers
Share to Cancer-related molecular mechanisms research

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 37159321
DOI: 10.1109/tmi.2023.3274652

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors From Pathological Images and Multi-Omics DataPMID 37159321Read ↗

Spatial multi-omics and deep learning reveal fingerprints of immunotherapy response and resistance in hepatocellular carcinomaSemantic proximity Read ↗

Integrating multi-omics data through deep learning for accurate cancer prognosis predictionSemantic proximity Read ↗

DeepMOCCA: A pan-cancer prognostic model identifies personalized prognostic markers through graph attention and multi-omics data integrationSemantic proximity Read ↗

Integrating cellular graph embeddings with tumor morphological features to predict in-silico spatial transcriptomics from H&E imagesSemantic proximity Read ↗

Digital profiling of cancer transcriptomes from histology images with grouped vision attentionSemantic proximity Read ↗

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE SlidesSemantic proximity Read ↗

Graph attention-based fusion of pathology images and gene expression for prediction of cancer survivalSemantic proximity Read ↗

Spatial transcriptome reveals histology-correlated immune signature learnt by deep learning attention mechanism on H&E–stained images for ovarian cancer prognosisSemantic proximity Read ↗

MOSBY enables multi-omic inference and spatial biomarker discovery from whole slide imagesSemantic proximity Read ↗

Transformer with convolution and graph-node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image.Semantic proximity Read ↗

AUTOSurv: Interpretable Deep Learning Framework for Cancer Survival Analysis Incorporating Clinical and Multi-omics DataSemantic proximity Read ↗

Pan-cancer integrative histology-genomic analysis via multimodal deep learningSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.