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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,...
Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...
Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...

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Related Experiment Video

Updated: May 13, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

DNdisorder: predicting protein disorder using boosting and deep networks.

Jesse Eickholt1, Jianlin Cheng

  • 1Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.

BMC Bioinformatics
|March 19, 2013
PubMed
Summary

Predicting protein disorder is crucial for understanding cell functions and drug targets. A new deep learning method, DNdisorder, accurately identifies these disordered regions using sequence-based analysis.

Related Experiment Videos

Last Updated: May 13, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Area of Science:

  • Biochemistry
  • Computational Biology
  • Structural Biology

Background:

  • Proteins can contain intrinsically disordered regions (IDRs) that lack stable tertiary structures.
  • IDRs play vital roles in cellular functions and are emerging as significant drug targets.

Purpose of the Study:

  • To develop and evaluate a novel sequence-based computational method for predicting protein disorder.
  • To assess the performance of the prediction method in the CASP10 experiment.

Main Methods:

  • Utilized boosted ensembles of deep neural networks for prediction.
  • Employed a 10-fold cross-validation on a dataset of 723 proteins.
  • Incorporated a boosting procedure to iteratively enhance prediction accuracy.

Main Results:

  • Achieved an average balanced accuracy of 0.82 and an area under the ROC curve (AUC) of 0.90.
  • Demonstrated steady improvement in balanced accuracy and AUC through the boosting procedure.
  • Showed competitive performance against state-of-the-art disorder predictors on CASP9 and CASP10 datasets.

Conclusions:

  • The developed method, DNdisorder, provides a robust approach for predicting protein disorder.
  • DNdisorder is accessible as a web service for broader research application.