Supplementary Materials of Sparse Optimal Scoring
by Chenlei Leng.
The paper:
Sparse optimal scoring for multiclass
cancer diagnosis and biomarker detection using microarray data.
Motivation
Gene expression data sets hold the promise to provide
cancer diagnosis on the molecular level. However, using all the gene profiles
for diagnosis may be suboptimal. Detection of the molecular
signatures not only reduces the number of genes needed for
discrimination purposes, but may
elucidate the roles they play in the biological processes.
Therefore, a central part of diagnosis is to detect
a small set of tumor biomarkers which can be used for
accurate multiclass cancer classification. This task calls for
effective multiclass classifiers with build-in biomarker selection mechanism.
Result
We propose the sparse optimal scoring (SOS) method for
multiclass cancer characterization. SOS is a simple prototype classifier
in which predictive biomarkers can be automatically
determined together with accurate classification.
Thus, SOS differentiates itself from many other commonly used classifiers,
where gene preselection must be applied before classification. We
obtain satisfactory performance while applying SOS to several public data sets.
Figure 1, Figure 2 and
Figure 3.
Further results for Brown dataset and SRBCT dataset.
Additional datasets:
GCM datset.
Brown dataset.