Rasch-based high-dimensionality data reduction and class prediction with applications to microarray gene expression data

Computer Science – Artificial Intelligence

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

Class prediction is an important application of microarray gene expression data analysis. The high-dimensionality of microarray data, where number of genes (variables) is very large compared to the number of samples (obser- vations), makes the application of many prediction techniques (e.g., logistic regression, discriminant analysis) difficult. An efficient way to solve this prob- lem is by using dimension reduction statistical techniques. Increasingly used in psychology-related applications, Rasch model (RM) provides an appealing framework for handling high-dimensional microarray data. In this paper, we study the potential of RM-based modeling in dimensionality reduction with binarized microarray gene expression data and investigate its prediction ac- curacy in the context of class prediction using linear discriminant analysis. Two different publicly available microarray data sets are used to illustrate a general framework of the approach. Performance of the proposed method is assessed by re-randomization scheme using principal component analysis (PCA) as a benchmark method. Our results show that RM-based dimension reduction is as effective as PCA-based dimension reduction. The method is general and can be applied to the other high-dimensional data problems.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Rasch-based high-dimensionality data reduction and class prediction with applications to microarray gene expression data does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Rasch-based high-dimensionality data reduction and class prediction with applications to microarray gene expression data, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Rasch-based high-dimensionality data reduction and class prediction with applications to microarray gene expression data will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-638207

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.