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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

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Related Experiment Video

Updated: May 11, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Steiner tree methods for optimal sub-network identification: an empirical study.

Afshin Sadeghi1, Holger Fröhlich

  • 1Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms Universitat Bonn, Dahlmannstr 2, 53113 Bonn, Germany. sadeghi.afshin@yahoo.com

BMC Bioinformatics
|May 1, 2013
PubMed
Summary
This summary is machine-generated.

A modified shortest paths algorithm accurately identifies biological sub-networks, significantly outperforming exact methods in speed. This approach is crucial for systems biology network analysis and is available in the R-package SteinerNet.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Related Experiment Videos

Last Updated: May 11, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
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Published on: October 13, 2023

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Systems biology
  • Bioinformatics
  • Computational biology

Background:

  • Biological network analysis is key in systems biology.
  • Identifying sub-networks connecting seed proteins/genes is crucial.
  • The Steiner tree problem is NP-complete, hindering practical bioinformatics applications.

Purpose of the Study:

  • To systematically evaluate approximate and exact algorithms for biological network analysis.
  • To develop and assess a novel algorithm for merged Steiner trees.
  • To compare algorithm performance on a large human protein-protein interaction network.

Main Methods:

  • Systematic simulation study of four approximate and one exact algorithm.
  • Implementation and testing on a human protein-protein interaction network (~14,000 nodes, ~400,000 edges).
  • Devised a new algorithm for retrieving merged Steiner trees.

Main Results:

  • A modified Takahashi and Matsuyama shortest paths algorithm yielded accurate solutions.
  • This approximation algorithm was orders of magnitude faster than the exact approach.
  • The devised merged Steiner tree algorithm is effective for small seed lists.

Conclusions:

  • The modified shortest paths algorithm offers an efficient and accurate method for biological network analysis.
  • The merged Steiner tree algorithm provides a useful tool for specific network retrieval tasks.
  • All implemented methods are publicly available in the R-package SteinerNet.