Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
In order to overcome the limitation on small size of gene
datasets, many meta-classification methods which
ensemble classifiers from different datasets have been
developed. However, due to discrepancies of the
characteristics among multiple heterogeneous datasets,
the number of common and significant genes is usually
small. Instead of matching common genes between
heterogeneous datasets, we propose a novel solution,
alternative feature mapping approach (AFM), to utilize
related and discriminative gene expressions while not
necessarily having exact matches. Genes in the training
dataset are clustered and mapped to the test dataset as
gene groups. Through analyzing the correlation within
gene groups, significant genes can be matched and
dataset dissimilarity factors can be used as weights for
meta-classification. We conducted experiments
consisting of 10 heterogeneous datasets with different
cancer types and platforms. Our experiments show that
classification performance is greatly improved using
suitable significant genes selected by AFM, and weight
voting method based on AFM provides more reliability
for meta-classification.
