Estimating dataset size requirements for classifying DNA microarray data.
A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves. It also includes a permutation test procedure to assess the statistical significance of classification performance for a given dataset size. This procedure is applied to several molecular classification problems representing a broad spectrum of levels of complexity.
Mukherjee
Sayan
S
Whitehead Institute/Massachusetts Institute of Technology Center for Genome Research, Cambridge, MA 02139, USA. sayan@genome.wi.mit.edu
Tamayo
Pablo
P
Rogers
Simon
S
Rifkin
Ryan
R
Engle
Anna
A
Campbell
Colin
C
Golub
Todd R
TR
Mesirov
Jill P
JP
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000145:classification / Q000379:methods*; D006801:Humans; D008958:Models, Molecular; D009369:Neoplasms / Q000145:classification* / Q000235:genetics* / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis