포트래이 인사이트
데이터 분석과 AI 기술, 그리고 포트래이가 만들어가는 플랫폼의 생각을 전합니다.
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Biologically informed cell typing in the tumor microenvironment with lightweight LLMs
The tumor microenvironment (TME) is a complex ecosystem of diverse cell types that interact dynamically, influencing tumor development and therapy response. Accurate cell type annotation in the TME...
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Estimation of homologous recombination deficiency map from spatial transcriptomics
Homologous recombination deficiency (HRD) is a predictive biomarker for various anti-cancer drugs such as poly(ADP-ribose) polymerase inhibitors (PARPi) and platinum-based chemotherapeutic agents...
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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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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...
포트래이-
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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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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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...
포트래이-
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...
포트래이-
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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