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相关概念视频

Protein Networks02:26

Protein Networks

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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,...
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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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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Genome-wide Association Studies-GWAS01:11

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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...
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Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

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Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
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Genetic Variation01:25

Genetic Variation

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
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相关实验视频

Updated: Sep 10, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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用知识图形辅助的贝叶斯主动学习来发现顶级基因相互作用

Braden Soper1, Michal Lisicki2,3, Mary Silva4

  • 1Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA. soper3@llnl.gov.

Scientific reports
|August 25, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了贝叶斯主动学习框架,以识别抑制HIV-1扩散的基因对. 该方法使用生物知识图表和批量多样化有效发现基因淘汰.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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相关实验视频

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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科学领域:

  • 计算生物学
  • 基因组学
  • 传染病模型

背景情况:

  • 对于功能基因组学,药物发现和疾病建模而言,多基因干扰的预测至关重要.
  • 开发哺乳动物系统的预测算法是具有挑战性的,因为数据有限,实验成本高.

研究的目的:

  • 开发贝叶斯主动学习框架,以发现抑制HIV-1扩散的对接宿主基因.
  • 利用生物知识图表和批量多样化来有效识别基因相互作用.

主要方法:

  • 实施了贝叶斯主动学习框架,其中包含生物知识图.
  • 采用计算效率高的批量多样化方法.
  • 在350多个宿主基因的双基因枯竭实验中对病毒载量测量的数据集进行了评估.

主要成果:

  • 该框架迅速确定了有效的基因淘汰对来减少HIV-1病毒载量.
  • 整合侧面信息 (知识图) 提高了早期积极学习的表现.
  • 批量多样化在后期阶段显著提高了业绩 (高数据制度).

结论:

  • 开发的框架有效地识别基因对以抑制HIV-1模型中的病毒扩散.
  • 这种方法可用于在其他生物环境中探索基因相互作用,例如合成致死性和表观性.
  • 这种方法为功能基因组学和疾病建模提供了成本效益和快速的方法.