Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Remics: a redescription-based framework for multi-omics analysis.

Frontiers in cell and developmental biology·2026

Same author

SARS-CoV-2 infectivity can be modulated through bacterial grooming of the glycocalyx.

mBio·2025

Same author

AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.

Briefings in bioinformatics·2024

Same author

Correction for Zhu et al., "Phylogeny-Aware Analysis of Metagenome Community Ecology Based on Matched Reference Genomes while Bypassing Taxonomy".

mSystems·2024

Same author

Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production.

mSystems·2024

Same author

Standardized multi-omics of Earth's microbiomes reveals microbial and metabolite diversity.

Nature microbiology·2022

Search research articles

Related Experiment Video

Updated: Jun 4, 2026

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

Evaluation of BIC and cross validation for model selection on sequence segmentations.

Niina Haiminen¹, Heikki Mannila

¹HIIT, University of Helsinki and Helsinki University of Technology, P.O. Box 68, FI-00014 University of Helsinki, Finland. niina.haiminen@cs.helsinki.fi

International Journal of Data Mining and Bioinformatics

|March 2, 2011

Summary

This summary is machine-generated.

This study evaluates model selection techniques, Bayesian Information Criterion (BIC) and Cross Validation (CV), for genomic data segmentation. The methods effectively identify segments with varying characteristics in DNA sequences.

More Related Videos

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Related Experiment Videos

Last Updated: Jun 4, 2026

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Area of Science:

Genomics
Bioinformatics
Data Mining

Background:

Segmentation is crucial for analyzing sequential data, particularly large-scale genomic structures like isochores.
Determining the optimal number of segments is a significant challenge in data analysis.

Purpose of the Study:

To conduct extensive experimental studies on model selection techniques for data segmentation.
To evaluate the effectiveness of Bayesian Information Criterion (BIC) and Cross Validation (CV) in genomic data segmentation.

Main Methods:

Applied segmentation techniques to analyze sequential data, focusing on genomic structures.
Utilized Bayesian Information Criterion (BIC) and Cross Validation (CV) for model selection.
Investigated the impact of linear trends and outliers on segmentation accuracy.

Main Results:

Successfully identified segments with differing means or variances in genomic data.
Demonstrated the influence of common real-world data issues like linear trends and outliers.
Analyzed real DNA sequences based on codon, G + C, and bigram frequencies, and copy-number variation from CGH data.

Conclusions:

BIC and CV are effective model selection techniques for genomic data segmentation.
The presence of linear trends and outliers can impact segmentation results.
The study provides valuable insights for analyzing complex genomic datasets.