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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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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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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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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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Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Pedigree Analysis01:35

Pedigree Analysis

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Overview
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Updated: Jun 28, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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KDGene:通过交互式张量分解完成疾病基因预测的知识图表.

Xinyan Wang, Kuo Yang, Ting Jia

    Briefings in bioinformatics
    |April 12, 2024
    PubMed
    概括

    KDGene是一个新的框架,通过利用生物知识图和交互式张量分解来增强疾病基因预测. 这种方法改善了对分子机制研究的关键疾病相关基因的识别.

    科学领域:

    • 基因组学和生物信息学
    • 计算生物学 计算生物学
    • 系统生物学 系统生物学

    背景情况:

    • 准确识别与疾病相关的基因对于了解疾病机制至关重要.
    • 目前使用生物网络和深度学习的方法往往忽视了生物知识图中的复杂关系.
    • 现有的知识图嵌入技术在特定领域的生物数据上表现不佳.

    研究的目的:

    • 通过将生物知识图表中的复杂关系纳入疾病基因预测的先进框架.
    • 改进语义上类似的生物概念的表现,提高疾病基因识别准确度.
    • 为识别新型候选疾病基因提供可扩展的解决方案.

    主要方法:

    • 构建一个以疾病和基因为重点的生物知识图.
    • 开发KDGene,一个端到端的知识图完成框架,利用交互式张量分解.
    • 整合一个交互模块,以在张量分解中弥合实体和关系嵌入.

    主要成果:

    • KDGene显著优于现有的最先进的疾病基因预测和一般知识图嵌入方法.
    • 综合的生物分析验证了KDGene在准确识别新型候选疾病基因方面的能力.
    • 拟议的框架展示了可扩展性和有希望的结果,用于未来的实验验证.
    关键词:
    疾病 基因预测 基因预测完成知识图表的完成.张量分解分解 张量分解

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    结论:

    • 通过有效利用生物知识图形信息,KDGene为疾病基因预测提供了卓越的方法.
    • 该框架捕捉复杂关系的能力提高了疾病相关基因的准确性和发现.
    • 这项工作为寻求识别候选基因以进行进一步的湿实验的研究人员提供了宝贵的资源.