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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-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 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,...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

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Related Experiment Video

Updated: May 26, 2026

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
08:38

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Published on: March 3, 2015

Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and

Shashank Agarwal1, Feifan Liu, Hong Yu

  • 1Medical Informatics, College of Engineering and Applied Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, USA. agarwal@uwm.edu

BMC Bioinformatics
|December 14, 2011
PubMed
Summary

Two machine learning frameworks, Simple Classifier and OntoNorm, can effectively identify protein-protein interaction (PPI) articles and their study methods. These domain-independent tools achieve competitive performance in automated biomedical text mining tasks.

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Last Updated: May 26, 2026

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
08:38

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Published on: March 3, 2015

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Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

Published on: July 6, 2014

Identifying Protein-protein Interaction Sites Using Peptide Arrays
07:44

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Published on: November 18, 2014

Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Text Mining

Background:

  • Protein-protein interactions (PPIs) are crucial in biological processes.
  • Automated detection of PPI-relevant literature and study methods is a key challenge in text mining.
  • Existing methods often rely on domain-specific features, limiting their generalizability.

Purpose of the Study:

  • To develop and evaluate domain-independent machine learning frameworks for PPI-related text mining.
  • To create a binary classifier (Simple Classifier) for identifying PPI-relevant articles.
  • To develop a framework (OntoNorm) for mapping PPI articles to standardized interaction method ontologies (PSI-MI).

Main Methods:

  • Exploration of domain-independent features for machine learning model development.
  • Implementation of a binary classification model (Simple Classifier) for document relevance.
  • Development of an ontology mapping tool (OntoNorm) using PSI-MI standards.

Main Results:

  • The developed systems demonstrated competitive performance in the BioCreative challenge.
  • The Simple Classifier achieved a 60.8% F1-score for identifying relevant documents.
  • OntoNorm attained a 52.3% F1-score for mapping articles to interaction method ontologies.

Conclusions:

  • Domain-independent machine learning frameworks can effectively perform PPI-related text mining tasks.
  • The developed Simple Classifier and OntoNorm show promise for automated analysis of biomedical literature.
  • These tools offer a generalizable approach to identifying PPIs and their associated experimental methods.