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相关实验视频

Updated: Jul 2, 2025

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

深度学习模型用于使用目标临床基因组测序数据进行瘤类型预测.

Madison Darmofal^1,2, Shalabh Suman³, Gurnit Atwal^4,5,6

¹Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York.

Cancer discovery

|February 28, 2024

概括

一个新的深度学习模型,基因组衍生诊断组合 (GDD-ENS),使用向基因面板测序准确预测瘤类型. 这种方法与全基因组测序方法相竞争,并有助于诊断罕见癌症,以改善患者治疗.

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Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Published on: July 25, 2020

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

相关实验视频

Last Updated: Jul 2, 2025

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Published on: July 25, 2020

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

科学领域:

在瘤学瘤学.
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

基于组织学的癌症诊断具有挑战性,但瘤类型对于治疗决策至关重要.
基因组改变对瘤类型的诊断非常有用,但目前的方法往往在临床上是不可行的.
现有的瘤类型分类器面临着全基因组测序数据或受限癌症类型预测的局限性.

研究的目的:

使用向癌症基因组测序数据开发一种临床可行和准确的瘤类型分类模型.
创建基于深度神经网络的超参数组合,以提高诊断性能.
实现实时瘤类型预测,以指导临床治疗决策.

主要方法:

利用了通过向癌症基因组组测序的39,787个实体瘤的基因特征.
开发了基因组衍生诊断组合 (GDD-ENS),这是一个深度神经网络组合模型.
训练并验证了将38种不同的癌症类别中的瘤类型分类的模型.

主要成果:

在38种癌症类型中,GDD-ENS在高可信度预测中实现了93%的准确性.
该模型的性能与基于全基因组测序的方法相美.
GDD-ENS在诊断罕见的瘤类型和未知的原发性癌症方面表现出实用性,并有可能纳入临床信息.

结论:

GDD-ENS提供了一种临床可行且准确的方法,用于使用向基因组进行瘤类型预测.
该模型的性能与更复杂的全基因组测序方法相美.
将GDD-ENS集成到临床工作流中可以增强实时诊断能力,并指导癌症治疗决策.