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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.1K
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...
13.1K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

403
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
403
Weighted Mean00:57

Weighted Mean

4.9K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
4.9K
Ribosome Profiling02:24

Ribosome Profiling

3.5K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.5K

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

Cross-Generational Validation of a Feedforward Neural Network for Milk Yield Prediction in Dairy Cattle.

Animals : an open access journal from MDPI·2026
Same author

BioGAN: Enhancing Transcriptomic Data Generation with Biological Knowledge.

Bioengineering (Basel, Switzerland)·2025
Same author

In-Silico Identification of Novel Pharmacological Synergisms: The Trabectedin Case.

International journal of molecular sciences·2024
Same author

RGMQL: scalable and interoperable computing of heterogeneous omics big data and metadata in R/Bioconductor.

BMC bioinformatics·2022
Same author

Identification, semantic annotation and comparison of combinations of functional elements in multiple biological conditions.

Bioinformatics (Oxford, England)·2021
Same author

Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers.

NPJ systems biology and applications·2021
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
查看所有相关文章

相关实验视频

Updated: Jun 10, 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

647

生物加权LASSO:提高基因表达数据分析中的功能解释性.

Sofia Mongardi1, Silvia Cascianelli1, Marco Masseroli1

  • 1Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milan 20133, Italy.

Bioinformatics (Oxford, England)
|October 16, 2024
PubMed
概括
此摘要是机器生成的。

这项研究为基因表达数据引入了一种新的特征选择方法. 它整合了生物知识,以改善基因识别和解释性,优于标准方法.

更多相关视频

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

5.4K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K

相关实验视频

Last Updated: Jun 10, 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

647
Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

5.4K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K

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
  • 基因组学就是基因组学.

背景情况:

  • 特性选择对于基因表达数据分析至关重要.
  • 目前的方法往往缺乏生物解释性.
  • 整合先前的生物知识可以增强分析.

研究的目的:

  • 开发一种整合性的特征选择方法.
  • 将加权的LASSO与生物事先知识相结合.
  • 为了提高预测性能和生物解释性.

主要方法:

  • 开发了一种嵌入式整合方法来进行特征选择.
  • 创建了一个具有生物相关性的新分数.
  • 综合加权LASSO与生物知识在一个步骤.

主要成果:

  • 拟议的方法确定了最具预测性的基因.
  • 它显著提高了结果的生物解释性.
  • 在实验中表现优于标准LASSO.

结论:

  • 综合性方法有效地平衡了预测能力和生物洞察力.
  • 这种方法为基因表达分析提供了一个更易于解释的替代方案.
  • 代码是公开可用的可复制性.