alexa Evolutionary algorithms for finding optimal gene sets in microarray prediction.
Genetics

Genetics

Advancements in Genetic Engineering

Author(s): Deutsch JM

Abstract Share this page

Abstract MOTIVATION: Microarray data has been shown recently to be efficacious in distinguishing closely related cell types that often appear in different forms of cancer, but is not yet practical clinically. However, the data might be used to construct a minimal set of marker genes that could then be used clinically by making antibody assays to diagnose a specific type of cancer. Here a replication algorithm is used for this purpose. It evolves an ensemble of predictors, all using different combinations of genes to generate a set of optimal predictors. RESULTS: We apply this method to the leukemia data of the Whitehead/MIT group that attempts to differentially diagnose two kinds of leukemia, and also to data of Khan et al. to distinguish four different kinds of childhood cancers. In the latter case we were able to reduce the number of genes needed from 96 to less than 15, while at the same time being able to classify all of their test data perfectly. We also apply this method to two other cases, Diffuse large B-cell lymphoma data (Shipp et al., 2002), and data of Ramaswamy et al. on multiclass diagnosis of 14 common tumor types. AVAILABILITY: http://stravinsky.ucsc.edu/josh/gesses/.
This article was published in Bioinformatics and referenced in Advancements in Genetic Engineering

Relevant Expert PPTs

Relevant Speaker PPTs

Recommended Conferences

  • International Conference on Epigenetics 2017
    November 13-15, 2017 Frankfurt, Germany
  • International Conference on Genetic Counseling and Genomic Medicine
    February 12-13, 2018 Madrid, Spain

Relevant Topics

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords