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

Emerging patterns and gene expression data.

J Li1, L Wong

  • 1Kent Ridge Digital Labs, 21 Heng Mui Keng Terrace, Singapore, 119613. jinyan@krdl.org.sg

Genome Informatics. International Conference on Genome Informatics
|January 16, 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

The ribosomal DNA loci in Plasmodium falciparum accumulate mutations independently.

Journal of molecular biology·1995
Same author

Binding of phenylarsenoxide to Arg-tRNA protein transferase is independent of vicinal thiols.

Biochemistry·1995
Same author

The oncogene qin codes for a transcriptional repressor.

Cancer research·1995
Same author

Non-receptor cytosolic protein tyrosine kinases from various rat tissues.

Biochimica et biophysica acta·1995
Same author

Mutagenesis in the C-terminal region of human interleukin 5 reveals a central patch for receptor alpha chain recognition.

Proceedings of the National Academy of Sciences of the United States of America·1995
Same author

Identification and characterization of a novel related adhesion focal tyrosine kinase (RAFTK) from megakaryocytes and brain.

The Journal of biological chemistry·1995
Same journal

Linear regression models predicting strength of transcriptional activity of promoters.

Genome informatics. International Conference on Genome Informatics·2012
Same journal

Sign: large-scale gene network estimation environment for high performance computing.

Genome informatics. International Conference on Genome Informatics·2012
Same journal

Docking-calculation-based method for predicting protein-RNA interactions.

Genome informatics. International Conference on Genome Informatics·2012
Same journal

Mechanism of cell cycle disruption by multiple p53 pulses.

Genome informatics. International Conference on Genome Informatics·2012
Same journal

Database for crude drugs and Kampo medicine.

Genome informatics. International Conference on Genome Informatics·2012
Same journal

A dynamic programming algorithm to predict synthesis processes of tree-structured compounds with graph grammar.

Genome informatics. International Conference on Genome Informatics·2011
See all related articles

Researchers identified gene expression patterns linked to disease states. These patterns accurately predict if a cell is normal or cancerous, offering insights into disease correlation.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene expression profiling is crucial for understanding disease states.
  • Identifying correlations between gene expression and diseases remains a challenge.

Purpose of the Study:

  • To discover significant gene expression patterns correlated with disease states.
  • To develop a method for predicting normal versus cancerous cells based on gene expression.

Main Methods:

  • Utilized an entropy-oriented discretization method to identify emerging patterns.
  • Defined gene groups (patterns) based on specific expression intervals and cell-type frequency.
  • Applied the method to a colon tumor dataset.

Main Results:

Related Experiment Videos

  • Discovered gene groups (patterns) significantly correlated with disease states.
  • Achieved up to 90% frequency for identified patterns in the colon tumor dataset.
  • Demonstrated the patterns' ability to predict cell normalcy or cancerous state.

Conclusions:

  • The discovered gene expression patterns provide valuable insights into disease correlation.
  • This pattern-based approach enables accurate prediction of cellular disease states.
  • The method holds potential for diagnostic applications in oncology.