Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Protein Networks02:26

Protein Networks

4.1K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

6.4K
Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
6.4K
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.1K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.1K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.2K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
14.2K
Spontaneous and Induced Mutations01:30

Spontaneous and Induced Mutations

145
Spontaneous mutations arise infrequently during DNA replication due to errors in the process. A key factor behind these errors is tautomeric shifts in nitrogenous bases, where bases transition from keto to enol forms or amino to imino forms. This shift can alter base-pairing rules, leading to mutations. Additionally, reactive oxygen species (ROS) arising from aerobic metabolism can damage DNA, resulting in depurination (loss of a purine base) or depyrimidination (loss of a pyrimidine base).
145
Mutagenicity and Carcinogenicity01:25

Mutagenicity and Carcinogenicity

1.4K
Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...
1.4K

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

A case of rhabdoid meningioma originating from the optic nerve.

Neuropathology : official journal of the Japanese Society of Neuropathology·2025
Same author

Development a glycosylated extracellular vesicle-derived miRNA Signature for early detection of esophageal squamous cell carcinoma.

BMC medicine·2025
Same author

Prevalence of malnutrition among adult inpatients in China: a nationwide cross-sectional study.

Science China. Life sciences·2025
Same author

Improving Diagnostic Accuracy in Acute Pulmonary Embolism: Insights from Spectral Dual-energy CT.

Current medical imaging·2025
Same author

Infiltrating plasma cells maintain glioblastoma stem cells through IgG-Tumor binding.

Cancer cell·2025
Same author

Differential gene expression in Clostridium perfringens during pre-and post-infection phases and in jejunal tissues of broilers with necrotic enteritis induced by Clostridium perfringens alone and its coinfection with Eimeria.

Poultry science·2024
Same journal

A Denoising Adversarial Model Based on Hyperellipsoidal Knowledge Representation Learning for DTI Prediction.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Improving Cancer Driver Gene Prediction using Biological knowledge-guided Prompts for LLM.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-task Learning Method.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

A Multi-Modal Framework for Phage-Host Interaction Prediction Using Multi-View Contrastive Learning.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Decoding Gene-Disease Associations with Computational Methods: A Survey.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

A Competitive Coevolution-based Cancer Driver Pathway Identification Algorithm for Maximizing Coverage, Mutual Exclusivity, and Subnet Importance.

IEEE transactions on computational biology and bioinformatics·2026
查看所有相关文章

相关实验视频

Updated: Sep 11, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K

MDSGCN:通过签名图形卷积网络预测多种类型的突变药物关联.

Haisong Feng, Xiaosong Wang, Fan Lin

    IEEE transactions on computational biology and bioinformatics
    |August 14, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一个深度学习模型,MDSGCN,用于预测癌症治疗的基因突变-药物关联. 该模型准确地识别了敏感或耐药的突变-药物关系,有助于个性化癌症治疗.

    更多相关视频

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    894
    A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
    13:34

    A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

    Published on: April 6, 2016

    10.3K

    相关实验视频

    Last Updated: Sep 11, 2025

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
    07:35

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

    Published on: October 13, 2023

    1.7K
    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    894
    A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
    13:34

    A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

    Published on: April 6, 2016

    10.3K

    科学领域:

    • 在瘤学瘤学.
    • 生物信息学是一种生物信息学.
    • 计算生物学 计算生物学

    背景情况:

    • 癌症是一个主要的全球健康问题,由基因突变驱动.
    • 了解突变与药物相互作用对于癌症治疗至关重要,但具有挑战性.
    • 目前评估突变与药物关联的方法是艰苦而昂贵的.

    研究的目的:

    • 开发一种新的深度学习模型,用于预测多种类型的突变-药物关联.
    • 提高确定临床相关突变与药物关系的效率和准确性.
    • 通过预测药物敏感性或耐药性来帮助个性化癌症治疗策略.

    主要方法:

    • 开发了一个签名图形卷积网络 (MDSGCN) 模型.
    • 突变药物协会作为一个签署的双方网络.
    • 该模型学习了子图的结构特征和集成的生物相似性 (突变-突变和药物-药物).

    主要成果:

    • 与现有最先进的方法相比,MDSGCN模型表现出优越的性能.
    • 实验结果证实了该模型在预测突变与药物相关性的有效性.
    • 一个案例研究强调了该模型发现新突变药物关系及其类型的能力.

    结论:

    • MDSGCN提供了一种强大的计算方法,用于预测癌症中的突变与药物相关性.
    • 该模型可以加速发现向疗法并改善癌症治疗结果.
    • 这项工作促进了深度学习和网络分析在精密瘤学中的整合.