Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Pattern identification in biogeography.

Ganeshkumar Ganapathy1, Barbara Goodson, Robert Jansen

  • 1Department of Computer Sciences, The University of Texas at Austin, Austin, TX 78712, USA. gsgk@cs.utexas.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|November 7, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Min-frame transformation enables more sensitive viral genome alignment.

bioRxiv : the preprint server for biology·2026
Same author

TIPP-SD: A new method for species detection in microbiomes.

PLoS computational biology·2026
Same author

Phylogenetic Placement Using SCAMPP and Batch-SCAMPP.

Methods in molecular biology (Clifton, N.J.)·2026
Same author

External Validation of the PRI-MUS Scoring System: A Secondary Analysis of the OPTIMUM Randomized Controlled Trial.

The Journal of urology·2026
Same author

The Earth BioGenome Project Phase II: illuminating the eukaryotic tree of life.

Frontiers in science·2026
Same author

Antenatal Pulmonary Hypoplasia Expands the Variety of Phenotypes Associated With MED12-Related Disorders.

Prenatal diagnosis·2025
Same journal

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same journal

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics·2024
See all related articles

This study formalizes pattern identification in historical biogeography, introducing metrics to compare area cladograms and algorithms for finding the maximum agreement area cladogram (MAAC). It highlights the computational complexity of MAAC identification for multiple cladograms.

Area of Science:

  • Historical biogeography
  • Phylogenetic comparative methods
  • Computational phylogenetics

Background:

  • Area cladograms are crucial for biogeographical inference, but methods for comparing them lack formalization.
  • Identifying common patterns aids in understanding historical biogeographical relationships and vicariance events.

Purpose of the Study:

  • To provide the first rigorous formalization of pattern identification among area cladograms.
  • To develop novel metrics for comparing area cladograms.
  • To introduce efficient algorithms for determining cladogram similarity and consensus.

Main Methods:

  • Formalization of pattern identification problems in biogeography.
  • Development of metrics for comparing area cladograms.
  • Algorithm design for Maximum Agreement Area Cladogram (MAAC) computation.

Related Experiment Videos

  • Complexity analysis of MAAC for multiple binary area cladograms.

    • Main Results:

    • The study introduces a formal framework for comparing area cladograms.
    • Efficient algorithms are presented for finding the MAAC of two area cladograms.
    • It is proven that finding the MAAC of several binary area cladograms is NP-hard.
    • A linear-time algorithm is described for checking the identity of two area cladograms.

    Conclusions:

    • The developed methods and algorithms advance the field of historical biogeography and biogeographical inference.
    • The findings provide essential tools for analyzing and comparing phylogenetic area data.
    • Understanding the computational complexity of MAAC is critical for future research in phylogenetics.