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

Brain Imaging01:14

Brain Imaging

260
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
260

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

LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Breathing New Life into Small Object Detection with Detection-Oriented Rectification.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction.

Science advances·2026
Same author

Interpretable graph deep learning framework for drug synergy prediction by integrating functional and clinical similarities.

NPJ digital medicine·2026
Same author

Pre-Fluorinated SEI by Catalyzing a Parasitic Reaction Toward Stable Silicon Anodes.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Stress-Mediated Lattice Reconstruction Regenerates Spent LiFePO<sub>4</sub> Cathodes.

Advanced materials (Deerfield Beach, Fla.)·2026
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
查看所有相关文章

相关实验视频

Updated: Jul 23, 2025

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

807

脑成像遗传学的多任务深度特征选择方法

Chenglin Yu, Shu Zhang, Muheng Shang

    IEEE/ACM transactions on computational biology and bioinformatics
    |July 11, 2023
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的多任务深度特征选择 (MTDFS) 方法,以更好地识别影响脑成像定量特征 (QTs) 的遗传风险因素. MTDFS有效地模拟复杂的关系,在脑成像遗传学研究中表现优于传统的线性模型.

    更多相关视频

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.6K
    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
    11:28

    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

    Published on: June 30, 2018

    11.7K

    相关实验视频

    Last Updated: Jul 23, 2025

    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

    807
    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.6K
    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
    11:28

    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

    Published on: June 30, 2018

    11.7K

    科学领域:

    • 神经科学是一个神经科学.
    • 遗传学 是一个遗传学.
    • 计算生物学 计算生物学

    背景情况:

    • 大脑成像遗传学试图将遗传变异与大脑结构和功能联系起来.
    • 通常使用线性模型,但可能无法捕捉到对成像特征的复杂遗传影响.
    • 确定神经疾病的遗传风险因素需要先进的分析方法.

    研究的目的:

    • 为大脑成像遗传学提出一种新的多任务深度特征选择 (MTDFS) 方法.
    • 模拟脑成像定量特征 (QTs) 与单核酸多态 (SNP) 等遗传因素之间的复杂,非线性关联.
    • 提高显著遗传风险位置的识别.

    主要方法:

    • 开发了一种多任务深度神经网络,以捕捉QT和SNP之间的复杂关系.
    • 整合了多任务一对一层,并结合了有效的特征选择的惩罚.
    • 将拟议的MTDFS方法与多任务线性回归 (MTLR) 和单任务特征选择 (DFS) 进行了比较.

    主要成果:

    • MTDFS成功地模拟了QT和SNP之间的非线性关系.
    • 与MTLR和DFS相比,该方法在识别QT-SNP关联方面表现优异.
    • 在真实神经成像遗传数据上,MTDFS在关系识别和特征选择方面都被证明是有效的.

    结论:

    • 在脑成像遗传学中,MTDFS提供了一种强大的方法来识别遗传风险位点.
    • 该方法通过有效地处理对大脑成像特征的复杂遗传影响,使该领域取得了进展.
    • MTDFS是现有的脑成像遗传学方法的宝贵补充.