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

A tree kernel to analyse phylogenetic profiles.

Jean-Philippe Vert1

  • 1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, 611-0011, Japan. Jean-Philippe.Vert@mines.org

Bioinformatics (Oxford, England)
|August 10, 2002
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

Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review.

Statistical science : a review journal of the Institute of Mathematical Statistics·2025
Same author

Multicondition and multimodal temporal profile inference during mouse embryonic development.

Genome research·2025
Same author

Sceptic: pseudotime analysis for time-series single-cell sequencing and imaging data.

Genome biology·2025
Same author

Multi-condition and multi-modal temporal profile inference during mouse embryonic development.

bioRxiv : the preprint server for biology·2024
Same author

LSMMD-MA: scaling multimodal data integration for single-cell genomics data analysis.

Bioinformatics (Oxford, England)·2023
Same author

A benchmark of computational pipelines for single-cell histone modification data.

Genome biology·2023
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

We introduce a new method to measure similarity between protein phylogenetic profiles, crucial for predicting gene function. This tree kernel approach enhances accuracy by incorporating evolutionary relationships among species.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Protein phylogenetic profiles encode protein presence/absence across genomes.
  • Correlated evolution of proteins in complexes/pathways leads to similar profiles.
  • Measuring profile similarity is key for predicting protein function.

Purpose of the Study:

  • Develop an evolutionarily relevant method to measure similarity between protein phylogenetic profiles.
  • Improve function prediction methods for genes using phylogenetic profiles.

Main Methods:

  • Mapping phylogenetic profiles to a high-dimensional vector space.
  • Developing the 'tree kernel' algorithm to compute inner products in this space.
  • Applying kernel-based methods like Support Vector Machines (SVM) and Kernel Principal Component Analysis (KPCA).

Related Experiment Videos

Main Results:

  • The tree kernel effectively incorporates evolutionarily relevant information into profile analysis.
  • SVMs using the tree kernel show improved gene function prediction accuracy compared to naive kernels.
  • KPCA demonstrates the tree kernel's sensitivity to evolutionarily relevant variations in profiles.

Conclusions:

  • The tree kernel provides a powerful tool for analyzing phylogenetic profiles.
  • This method enhances the prediction of gene function by leveraging evolutionary information.
  • The tree kernel is applicable to various kernel-based data mining and classification tasks.