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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,...
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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...
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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Multistage gene normalization and SVM-based ranking for protein interactor extraction in full-text articles.

Hong-Jie Dai1, Po-Ting Lai, Richard Tzong-Han Tsai

  • 1Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan, ROC. hongjie@iis.sinica.edu.tw

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 19, 2010
PubMed
Summary

We developed a novel multistage gene normalization algorithm for identifying interactors in protein-protein interactions (PPIs). This method significantly improved system performance, especially when utilizing full research papers.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Protein-protein interactions (PPIs) are crucial for cellular functions.
  • Accurate identification and normalization of interacting genes are essential for understanding PPI networks.
  • Existing methods face challenges in cross-species gene identification and leveraging full-text information.

Purpose of the Study:

  • To develop and evaluate a robust system for the interactor normalization task (INT).
  • To address the challenges of cross-species gene identification and full-text utilization in gene normalization (GN).
  • To improve the ranking of genes involved in PPIs based on normalized confidence.

Main Methods:

  • A multistage gene normalization (GN) algorithm was developed to identify interacting genes.
  • A novel ranking method was implemented to assign normalized confidence scores.
  • The system was designed to exploit information from various sections of scientific papers, including full text.

Main Results:

  • The developed system achieved an Area Under the Curve (AUC) of 0.43471.
  • The multistage GN algorithm improved system performance (AUC) by 1.719% compared to a one-stage approach.
  • Utilizing full text, as opposed to abstracts alone, resulted in a 22.6% higher INT AUC performance.

Conclusions:

  • The multistage GN algorithm effectively enhances the accuracy of interactor identification in PPIs.
  • Leveraging full-text scientific literature significantly boosts the performance of interactor normalization.
  • The developed approach offers a promising solution for advancing PPI research through improved gene normalization and ranking.