  In medical sciences, a biomarker is "a characteristic that is objectively
measured and evaluated as an indicator of normal biological processes,
pathogenic processes, or pharmacologic responses to a therapeutic
intervention". Molecular experiments are providing rapid and systematic
approaches to search for biomarkers, but because single-molecule biomarkers
have shown a disappointing lack of robustness for clinical diagnosis,
researchers have begun searching for distinctive sets of molecules, called
"biosignatures". However, the most popular statistics are not appropriate for
their identification, and the number of possible biosignatures to be tested is
frequently intractable. In the present work, we developed a "multivariate
filter" using genetic algorithms (GA) as a feature (gene) selector to optimize
a measure of intra-group cohesion and inter-group dispersion. This method was
implemented using Python and R (pyBioSig, available at
https://github.com/fredgca/pybiosig under LGPL) and can be manipulated via
graphical interface or Python scripts. Using it, we were able to identify
putative biosignatures composed by just a few genes and capable of recovering
multiple groups simultaneously in a hierarchical clustering, even ones that
were not recovered using the whole transcriptome, within a feasible length of
time using a personal computer. Our results allowed us to conclude that using
GA to optimize our new intra-group cohesion and inter-group dispersion measure
is a clear, effective, and computationally feasible strategy for the
identification of putative "omical" biosignatures that could support
discrimination among multiple groups simultaneously.
