TITLE:
Integrative Self-Organizing Map—A Mean Pattern Model
AUTHORS:
Zihua Yang, Zhengrong Yang
KEYWORDS:
Integrative Study of Microarrays; Pattern Discovery; Differential Expressions; Self-Organizing Map
JOURNAL NAME:
Engineering,
Vol.5 No.10B,
October
31,
2013
ABSTRACT: We propose an integrative self-organizing map (iSOM) for
exploring differential expression patterns across multiple microarray
experiments. The algorithm is based on the assumption that observed
differential expressions are random samples of a mean pattern model which is
unknowna priori. The learning
mechanism of iSOM is similar to the conventional SOM. The mean pattern model
which underlies the proposed iSOM models mean differential expressions using a
one-dimension of mean differential expressions for the mean differential
expressions. The feature map of an iSOM model can be used to reveal correlation
between multiple medically/biologically related disease types or multiple
platform experiments for one disease. We illustrate applications of iSOM using
simulated data and real data.