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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,...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...

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Related Experiment Video

Updated: May 22, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Predicting protein function by multi-label correlated semi-supervised learning.

Jonathan Q Jiang1, Lisa J McQuay

  • 1Department of Information System, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong. qiajiang@cityu.edu.hk

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 19, 2012
PubMed
Summary
This summary is machine-generated.

Assigning biological functions to uncharacterized proteins is a challenge. A new Multi-label Correlated Semi-supervised Learning (MCSL) algorithm effectively predicts protein function by leveraging network correlations, overcoming data scarcity.

Related Experiment Videos

Last Updated: May 22, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Assigning biological functions to uncharacterized proteins is crucial in the postgenomic era.
  • Existing computational methods for protein function prediction often treat functional classes in isolation, leading to issues with scarce labeled data.
  • Protein-protein interaction (PPI) networks and functional class networks contain valuable relational information.

Purpose of the Study:

  • To develop a novel algorithm, Multi-label Correlated Semi-supervised Learning (MCSL), for improved protein function prediction.
  • To incorporate intrinsic correlations among functional classes into prediction models.
  • To leverage both PPI network and functional class network relationships.

Main Methods:

  • Proposed the Multi-label Correlated Semi-supervised Learning (MCSL) algorithm.
  • Leveraged relationships from both protein-protein interaction (PPI) networks and functional class networks.
  • Encoded intuition as regularized learning with intraclass and interclass consistency, extending graph-based learning with local and global consistency (LGC).

Main Results:

  • MCSL consistently outperformed several state-of-the-art methods in cross-validation tests on the yeast proteome.
  • The algorithm effectively addressed the challenge of scarce labeled data in protein function prediction.
  • Demonstrated improved accuracy in assigning biological functions to uncharacterized proteins.

Conclusions:

  • MCSL offers a significant advancement in computational protein function prediction.
  • Incorporating inter-functional class correlations improves prediction accuracy, especially with limited data.
  • The developed method provides a robust approach for understanding protein roles in biological systems.