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

DNA Microarrays02:34

DNA Microarrays

20.6K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
20.6K
Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K
Aggregates Classification01:29

Aggregates Classification

960
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
960
Classification of Leukocytes01:30

Classification of Leukocytes

4.9K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
4.9K
Classification of Systems-I01:26

Classification of Systems-I

543
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
543

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

Multi-structure segmentation in CBCT volumes: The ToothFairy2 challenge.

Medical image analysis·2026
Same author

A non-equilibrium oscillatory starch derivative driven by thermally induced phase separation: Application in dynamic anti-counterfeiting and wastewater treatment.

Carbohydrate polymers·2026
Same author

Impact of chitosan oligosaccharide on microbiota-metabolite-immune axis in natural aging.

Frontiers in nutrition·2026
Same author

Single-cell profiling reveals reprogrammed hierarchy and disrupted immune-stromal ecosystem in TP53-mutated AML.

Experimental hematology & oncology·2026
Same author

Epigenetic reprogramming for ocular aging and disease: Mechanisms, biomarkers, and the road to the clinic.

Progress in retinal and eye research·2026
Same author

Rainstorm regimes modulate cyanobacterial bloom dynamics in deep reservoirs: Synergistic effects of nutrient pulses and hydrological perturbations.

Limnology and oceanography·2026
Same journal

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026
Same journal

NeuroBooster: a domain-informed self-supervised learning paradigm tailored for brain MRI analysis.

IEEE journal of biomedical and health informatics·2026
Same journal

Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

IEEE journal of biomedical and health informatics·2026
Same journal

Improving Multi-Sensor Non-Invasive Glucose Detection through AI: A Domain Generalization Approach.

IEEE journal of biomedical and health informatics·2026
Same journal

Unmixing the Neck: Accurate Jugular Venous Pulse Detection From Wearable PPG.

IEEE journal of biomedical and health informatics·2026
Same journal

AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

IEEE journal of biomedical and health informatics·2026
查看所有相关文章

相关实验视频

Updated: Jan 11, 2026

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

7.9K

组合特征选择用于微阵列数据分类.

Xiaojian Ding, Pengcheng Shi, Xin Wang

    IEEE journal of biomedical and health informatics
    |November 13, 2025
    PubMed
    概括
    此摘要是机器生成的。

    一种新的组合特征选择方法 (EFSM) 通过提高特征选择稳定性来改善微阵列数据的分类. 在高维数据集中,EFSM有效地平衡了特征多样性和质量.

    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

    1.2K
    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    7.3K

    相关实验视频

    Last Updated: Jan 11, 2026

    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

    7.9K
    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

    1.2K
    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    7.3K

    科学领域:

    • 生物信息学是一种生物信息学.
    • 计算生物学 计算生物学
    • 机器学习 机器学习

    背景情况:

    • 微阵列数据分类面临着由于高维度和小样本大小的挑战,导致不稳定的特征选择.
    • 现有的组合特征选择方法往往无法有效平衡特征多样性和质量.

    研究的目的:

    • 引入一种新的组合特征选择方法 (EFSM),旨在提高特征选择稳定性和高维微阵列数据中的性能.
    • 解决传统方法在平衡特征多样性和预测准确性方面的局限性.

    主要方法:

    • EFSM利用随机的神经网络创建多种非线性特征映射 (视图),以生成一个强大的特征选择器候选池.
    • 采用了一种新的组合修剪技术,用半定义编程 (SDP) 问题来优化单个选择器的准确性和对式多样性.
    • 功能排名是使用Borda计数方法汇总的.

    主要成果:

    • 与9种最先进的特征选择方法相比,EFSM在15个生物数据集中显示出优越和稳定的性能.
    • 该方法在高维数据上使用流行的分类器进行测试时,实现了更好的分类准确性.

    结论:

    • 拟议的EFSM为高维微阵列数据分析中的特征选择提供了强大而有效的解决方案.
    • EFSM成功地平衡了特征多样性和质量,从而提高了分类性能和稳定性.