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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.
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Updated: May 11, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Probabilistic biological network alignment.

Andrei Todor¹, Alin Dobra, Tamer Kahveci

¹Department of Computer and Information Science and Engineering, University of Florida, PO Box 116120, E301 CSE Building, Gainesville, FL 32611-6120, USA. atodor@cise.ufl.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|May 25, 2013

Summary

This study introduces a novel method for aligning biological networks with probabilistic interactions. It efficiently handles complex topologies, yielding biologically relevant protein alignments.

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Published on: October 19, 2021

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Last Updated: May 11, 2026

A Practical Guide to Phylogenetics for Nonexperts

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Published on: February 5, 2014

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Systems Biology
Computational Biology
Bioinformatics

Background:

Molecular interactions are probabilistic, influenced by factors like size, abundance, and proximity.
Aligning biological networks is crucial for understanding molecular functions and pathways.
Existing methods often struggle with the complexity introduced by probabilistic interactions.

Purpose of the Study:

To develop a novel method for aligning biological networks that incorporates probabilistic interactions.
To efficiently characterize the vast search space of possible network topologies arising from probabilistic interactions.
To ensure biological relevance and meaningfulness in network alignments.

Main Methods:

Representing topological similarity between aligned molecules (proteins) using random variables.
Computing expected values of these random variables to quantify similarity.
Developing a novel method to efficiently and precisely characterize the search space of probabilistic network alignments.

Main Results:

The developed method efficiently handles the massive search space of probabilistic network topologies.
It produces novel and biologically meaningful protein alignments without sacrificing performance.
The method's effectiveness is validated against existing criteria and a new statistical coherence measure.

Conclusions:

The novel method provides an efficient and precise approach to aligning biological networks with probabilistic interactions.
It enhances biological relevance by accounting for the probabilistic nature of molecular interactions.
The method offers a statistically robust way to identify meaningful protein mappings and functional similarities.