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

On mining micro-array data by Order-Preserving Submatrix.

Lin Cheung1, David W Cheung, Ben Kao

  • 1Department of Computer Science, University of Hong Kong, Hong Kong. lcheung@cs.hku.hk

International Journal of Bioinformatics Research and Applications
|December 1, 2007
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

EpiATLAS - a reference for human epigenomic research.

bioRxiv : the preprint server for biology·2026
Same author

Correcting spatial transcriptomics data affected by a prevalent transcript leakage problem across platforms, species, and tissues.

bioRxiv : the preprint server for biology·2026
Same author

Spatial transcriptomics in Alzheimer's disease: technologies, challenges and discoveries.

Molecular neurodegeneration advances·2026
Same author

HIRA-mediated H3.3 deposition preserves hepatocyte cell identity during liver aging.

Research square·2026
Same author

SAKURA: a knowledge-guided approach to recovering important, rare signals from single-cell data.

Genome biology·2026
Same author

Multi-omic analysis of hepatocellular carcinoma reveals aberrant cis-regulatory changes and dysregulated retrotransposons with prognostic potential.

Communications biology·2025
Same journal

In silico analysis, annotation and characterisation of putative ESTs from Sorghum bicolor associated with heat stress.

International journal of bioinformatics research and applications·2015
Same journal

Docking analysis of gallic acid derivatives as HIV-1 protease inhibitors.

International journal of bioinformatics research and applications·2015
Same journal

Automatic segmentation of Potyviridae family polyproteins.

International journal of bioinformatics research and applications·2015
Same journal

Neural network and rough set hybrid scheme for prediction of missing associations.

International journal of bioinformatics research and applications·2015
Same journal

On the interconnection of stable protein complexes: inter-complex hubs and their conservation in Saccharomyces cerevisiae and Homo sapiens networks.

International journal of bioinformatics research and applications·2015
Same journal

Diversity and evolution of the envelope gene of dengue virus type 1 circulating in India in recent times.

International journal of bioinformatics research and applications·2015
See all related articles

This study introduces advanced pattern-based subspace clustering methods for analyzing complex datasets like DNA micro-arrays. The new approach enhances the discovery of diverse and significant patterns, improving data analysis capabilities.

Area of Science:

  • Data Mining
  • Bioinformatics
  • Machine Learning

Background:

  • Pattern-based subspace clustering identifies objects with similar rise and fall patterns within data subspaces.
  • Applications include analyzing DNA micro-array data, revealing biological insights.
  • Existing methods may struggle with discovering diverse or all significant patterns.

Purpose of the Study:

  • To develop pattern-based clustering methods capable of discovering various pattern shapes.
  • To ensure the discovery of all significant patterns within datasets.
  • To enhance the analysis of high-dimensional data, such as biological data.

Main Methods:

  • Extending the Order-Preserving Submatrix (OPSM) concept.
  • Developing a novel algorithm for mining OPSM.

Related Experiment Videos

  • Generalizing OPSM to encompass existing pattern-based clustering models.

    • Main Results:

    • A novel algorithm for mining Order-Preserving Submatrices (OPSM) was devised.
    • Demonstrated that OPSM can be generalized to cover most existing pattern-based clustering models.
    • Proposed several extensions to the original OPSM model for broader applicability.

    Conclusions:

    • The extended OPSM framework offers a generalized and powerful approach to pattern-based subspace clustering.
    • The developed methods improve the discovery of diverse and significant patterns in complex datasets.
    • This research advances the field of data mining with implications for bioinformatics and other domains.