A new nonlinear neural mapping (N2M) technique based on the combined u
se of Kohonen self-organizing map (KSOM), minimum spanning tree (MST),
and nonlinear mapping (NLM) is introduced for optimal test series sel
ection. With the N2M method, KSOM results are enhanced by the visualiz
ation of the actual distances between the loaded neurons from MST and
NLM. N2M provides an easily interpretable and comprehensible graphical
display which guides the selection of representative test series espe
cially when the number of individuals is high. In addition, structure-
activity relationships can be derived. The approach is open since any
information useful for data interpretation can be plotted by means of
graphical tools.