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Related Concept Videos

Protein Networks02:26

Protein Networks

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,...
Protein Networks02:26

Protein Networks

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,...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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 polypeptide...
Epistasis Analysis01:09

Epistasis Analysis

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...
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Gene function prediction with gene interaction networks: a context graph kernel approach.

Xin Li1, Hsinchun Chen, Jiexun Li

  • 1Department of Information Systems, City University of Hong Kong, Kowloon Tong, Hong Kong. xin.li@cityu.edu.hk

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|October 1, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for predicting gene functions using gene context graphs. The approach leverages indirect gene interactions, outperforming existing methods in accuracy.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Predicting gene function is crucial in the postgenomic era.
  • Gene interaction networks are commonly used to infer gene functions.
  • Previous methods often rely on a direct linkage assumption between interacting genes.

Purpose of the Study:

  • To develop a novel approach for inferring gene functions using gene context graphs.
  • To capture information from indirect gene interactions within biological networks.
  • To evaluate the proposed method against existing gene function prediction techniques.

Main Methods:

  • Utilized a kernel-based machine learning framework.
  • Designed a specific 'context graph kernel' to analyze gene interaction networks.
  • Conducted experiments on a dataset of p53-related genes.

Main Results:

  • Demonstrated the effectiveness of incorporating indirect gene interactions.
  • Showcased the empirical superiority of the context graph kernel approach.
  • Outperformed traditional linkage-assumption-based methods like diffusion kernels.

Conclusions:

  • The proposed context graph kernel method offers a more accurate way to predict gene functions.
  • Leveraging indirect gene interactions is advantageous for functional inference.
  • This approach advances the field of computational biology and gene function prediction.