Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Cancers Originate from Somatic Mutations in a Single Cell

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

Cancers Originate from Somatic Mutations in a Single Cell

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

Comprehensive multi-omic dissection and AI-prioritized target identification in inverted papilloma-associated sinonasal squamous cell carcinoma.

NPJ precision oncology·2026

Same author

Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.

Cancer research·2026

Same author

Living evidence-informed guideline on the early detection of oral squamous cell carcinoma and potentially malignant disorders: Light-based adjuncts to determine the need for biopsy, Version 2026 1.0.

Journal of the American Dental Association (1939)·2026

Same author

Accessible Clinical Tool for Prognosis and Chemotherapy Prediction in Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer in Diverse and Low-Resource Settings.

JCO global oncology·2026

Same author

Development and Validation of a Multimodal Clinical, Pathologic, and Genomic Model for Breast Cancer Recurrence.

medRxiv : the preprint server for health sciences·2026

Same author

Multi-modal AI for comprehensive breast cancer prognostication.

Nature communications·2026

Search research articles

相关实验视频

Updated: Jun 19, 2026

Generation of Prostate Cancer Patient Derived Xenograft Models from Circulating Tumor Cells

Generation of Prostate Cancer Patient Derived Xenograft Models from Circulating Tumor Cells

Published on: October 20, 2015

生成对抗网络从病理学,基因组学和放射学潜伏特征准确地重建泛癌组织学.

Frederick M Howard, Hanna M Hieromnimon, Siddhi Ramesh

bioRxiv : the preprint server for biology

|April 8, 2024

概括

HistoXGAN从人工智能特征中重建瘤组织结构,揭示生物洞察力并启用虚拟活检. 这种人工智能工具有助于理解癌症亚型和基因表达模式.

科学领域:

计算病理学计算病理学
人工智能在瘤学中的应用

更多相关视频

Generation of Tumor Organoids from Genetically Engineered Mouse Models of Prostate Cancer

Generation of Tumor Organoids from Genetically Engineered Mouse Models of Prostate Cancer

Published on: June 13, 2019

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

相关实验视频

Last Updated: Jun 19, 2026

Generation of Prostate Cancer Patient Derived Xenograft Models from Circulating Tumor Cells

Generation of Prostate Cancer Patient Derived Xenograft Models from Circulating Tumor Cells

Published on: October 20, 2015

Generation of Tumor Organoids from Genetically Engineered Mouse Models of Prostate Cancer

Generation of Tumor Organoids from Genetically Engineered Mouse Models of Prostate Cancer

Published on: June 13, 2019

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

数字病理学图像分析图像分析

背景情况:

人工智能 (AI) 模型分析瘤组织学以进行分类和分子特征识别.
目前的人工智能方法将组织学图像提炼成高层特征用于预测,但它们的生物含义往往不清楚.
了解癌症组织学中人工智能衍生特征的生物基础对于临床转化至关重要.

结论:

在计算病理学中,HistoXGAN为解释AI模型提供了一个强大的工具.
这种方法增强了对人工智能驱动的癌症分析的生物学基础的理解.
HistoXGAN促进了用于精密瘤学的更强大,更易于解释的人工智能工具的开发.