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相关概念视频

Epistasis Analysis01:09

Epistasis Analysis

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Gene-Environment Interactions01:20

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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Combinatorial Gene Control02:33

Combinatorial Gene Control

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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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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

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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.
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相关实验视频

Updated: Jun 15, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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IVEA:一种整合变异贝叶斯推理方法,用于预测增强剂-基因调节相互作用.

Yasumasa Kimura1,2,3, Yoshimasa Ono1, Kotoe Katayama2

  • 1DX Drug Discovery Department, Daiichi Sankyo RD Novare Co., Ltd., Edogawa-ku, Tokyo 134-8630, Japan.

Bioinformatics advances
|August 28, 2024
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概括

我们开发了IVEA,这是一种计算方法,通过估计基因促进剂和增强剂活动来预测增强剂-基因相互作用. 这种方法准确地识别了生物学上相关的调节关系,进步了我们对转录控制的理解.

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Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation
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科学领域:

  • 基因组学就是基因组学.
  • 分子生物学分子生物学
  • 计算生物学 计算生物学

背景情况:

  • 增强剂对于细胞类型特定的基因转录至关重要.
  • 确定增强剂-基因调控关系是基因组学中的一个重大挑战.
  • 计算方法对于准确推断这些相互作用至关重要.

研究的目的:

  • 提出一种新的计算方法,IVEA,用于预测增强剂-基因调节相互作用.
  • 根据转录突破机制估计促进和增强活动.
  • 计算增强剂-促进剂对目标基因转录的贡献.

主要方法:

  • 开发了使用变量贝叶斯推理的IVEA方法.
  • 集成的转录读取,染色质可访问性和染色质接触数据.
  • 基于转录性爆发 (爆发大小和频率) 的建模基因调节.

主要成果:

  • 在IVEA方法实现高预测准确性增强剂-基因相互作用.
  • 确定了生物相关的增强剂-基因调控关系.
  • 证明了估计促进者和增强者活动的有效性.

结论:

  • IVEA提供了一个准确的计算方法来推断增强剂-基因调控关系.
  • 该方法利用转录性爆破原理进行可靠的预测.
  • IVEA有助于更深入地了解基因调节.