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Related Concept Videos

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 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 Organization01:24

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.
Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

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Related Experiment Video

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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
13:56

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Published on: July 18, 2013

Document classification for mining host pathogen protein-protein interactions.

Lanlan Yin1, Guixian Xu, Manabu Torii

  • 1Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University, Washington, DC, USA.

Artificial Intelligence in Medicine
|May 18, 2010
PubMed
Summary
This summary is machine-generated.

This study developed an automated system to identify host-pathogen protein-protein interactions (HP-PPIs) in biomedical literature. Document classification effectively retrieves HP-PPIs, with feature selection improving system efficiency.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Bioinformatics

Background:

  • The vast biomedical literature contains critical data on human pathogens and host responses.
  • Extracting information on pathogenesis-related proteins, particularly host-pathogen protein-protein interactions (HP-PPIs), is challenging due to information overload.

Purpose of the Study:

  • To develop and evaluate an automated document classification system for identifying MEDLINE abstracts related to HP-PPIs.
  • To assess the effectiveness of feature selection methods in improving the performance of HP-PPI retrieval systems.

Main Methods:

  • An annotated corpus of 1360 MEDLINE abstracts was created.
  • Document classification systems were built using support vector machines (SVMs).
  • Feature selection methods including information gain (IG), chi(2) test, and specific mutual information (SI) were investigated. Performance was evaluated using normalized discounted cumulative gain (NDCG) and positive predictive value (PPV) via 10-fold cross-validation.

Main Results:

  • Classification systems utilizing features selected by IG and chi(2) test achieved high NDCG measures (0.83-0.89).
  • Systems using SI for feature selection showed lower NDCG values.
  • The classification system demonstrated a positive predictive value (PPV) of 50.7% for the top 10% of ranked documents, significantly outperforming the baseline PPV of 10.0%.

Conclusions:

  • Automated document classification systems can efficiently retrieve HP-PPI-related literature.
  • Feature selection is a valuable technique for reducing dimensionality and creating compact, efficient retrieval systems.