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포트래이 인사이트

데이터 분석과 AI 기술, 그리고 포트래이가 만들어가는 플랫폼의 생각을 전합니다.

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글 116편
AACR

Unveiling the tumor microenvironment of hepatocellular carcinoma using AI trained by spatial transcriptomics: A preliminary study to predict response to immunotherapy

Immune checkpoint inhibitors (ICIs), particularly targeting the PD-1 pathway, are promising in treating hepatocellular carcinoma (HCC). However, their variable effectiveness among individuals calls...

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AACR

Cancer region definition using spatial gene expression patterns by super resolution reconstruction algorithm for spatial transcriptomics data

Spatially resolved transcriptomics (ST) has enabled a variety of cancer research on the heterogeneity of tumor microenvironment. However, when identifying cancer boundaries based on ST, the limited...

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AACR

IAMSAM: Image-based analysis of molecular signatures using the Segment-anything model - Integrative analysis tool for tumor microenvironment

Spatial transcriptomics (ST) is a powerful approach for investigating gene expression patterns in tissues while preserving their spatial context. However, working with ST data presents challenges...

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AACR

Comparative spatial transcriptomic analysis of monoclonal antibodies targeting the same molecule: A comparison between cetuximab and panitumumab

In recent years, multiple therapeutic monoclonal antibodies have been developed that target the same molecules. Examples include trastuzumab and pertuzumab for HER2, cetuximab (CTX) and panitumumab...

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AACR

Automated tumor microenvironment analysis for multiple samples by image-based spatial transcriptomics on tissue microarray

Tissue Microarrays (TMAs), widely utilized in the field of pathology, have now found a powerful ally in Image-based Spatial Transcriptomics (ST). By analyzing various gene expression data with high...

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AACR

Development of a deep learning model for cell type mapping in colorectal cancer using H&E images leveraging image-based spatial transcriptomics data

The tumor microenvironment (TME) is crucial in colorectal cancer as it influences disease progression, treatment response, and patient outcomes, providing valuable insights for personalized therapi...

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AACR

Deep learning-based cell types scores in tumor microenvironment estimated by H&E images associated with PD-L1 status in lung adenocarcinoma

The spatial distribution of cell types in the tumor microenvironment (TME) is associated with functional status of tumor immunology and eventually affects the response to immuno-oncology treatment.

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AACR

Detecting cancer microbiota using unmapped RNA reads on spatial transcriptomics

Recently, the study of microbiota has emerged as a novel area in cancer research. While next-generation sequencing (NGS) technologies are becoming more prevalent in the area, the acquisition of spa...

포포트래이-
AACR

Data-driven discovery of cancer-specific targets for hepatocellular carcinoma using spatial transcriptomics

Hepatocellular carcinoma (HCC) is frequently diagnosed in advanced stages, with limited options available for systemic treatment. Given the significant investment of time, the high costs, and the...

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