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関連する概念動画

Protein Networks02:26

Protein Networks

4.1K
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

Genome-wide Association Studies-GWAS

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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...
509
Genetic Variation01:25

Genetic Variation

387
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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Updated: Sep 10, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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トップKの遺伝子相互作用の発見のためのバイジアン・アクティブ・ラーニング

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
まとめ
この要約は機械生成です。

この研究は,HIV-1の増殖を阻害する遺伝子ペアを特定するためのベイジアン・アクティブ・ラーニング・フレームワークを導入しています. この方法は,生物学的知識グラフとバッチ多様化を使用して効果的な遺伝子ノックダウンを効率的に発見します.

さらに関連する動画

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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関連する実験動画

Last Updated: Sep 10, 2025

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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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科学分野:

  • コンピュータ生物学
  • ゲノミクス
  • 感染症モデリング

背景:

  • マルチ遺伝子の混乱をシリコで予測することは,機能的ゲノミクス,薬剤発見,疾患モデリングに不可欠です.
  • 哺乳類システムの予測アルゴリズムの開発は,限られたデータと高い実験コストのために困難です.

研究 の 目的:

  • HIV-1の増殖を抑制するペアウェイズ宿主遺伝子ノックダウンを発見するためのベイジアンアクティブラーニングフレームワークを開発する.
  • 遺伝子相互作用の効率的な識別のために生物学的知識グラフとバッチの多様化を活用する.

主な方法:

  • バイオ知識グラフを組み込んだベイジアン・アクティブ・ラーニング・フレームワークを実装した.
  • 計算効率の良いバッチ多様化アプローチを採用した.
  • 350以上の宿主遺伝子の二重遺伝子の枯渇実験からのウイルス負荷測定データセットでフレームワークを評価した.

主要な成果:

  • このフレームワークはHIV-1ウイルス負荷を減らすのに有効な遺伝子ノックダウンペアを迅速に特定しました.
  • 側面情報 (知識グラフ) を取り入れることで,初期段階でのアクティブ・ラーニングの成果が向上しました.
  • バッチの多様化により,後の段階でのパフォーマンスは著しく向上した (高データ体制).

結論:

  • 開発されたフレームワークは,HIV-1モデルにおけるウイルス増殖を抑制するための遺伝子ペアを効率的に識別します.
  • このアプローチは,合成的致死性やエピスタシスなどの他の生物学的文脈における遺伝子相互作用の探索に一般化できる.
  • この方法は,機能的ゲノミクスと疾患モデリングに費用対効果の高い迅速なアプローチを提供します.