Molecular mechanisms of plant–pathogen interactions have been studied thoroughly but much
about them is still unknown. A better understanding of these mechanisms and the detection of new resistance
genes can improve crop production and food supply. Extracting this knowledge from available genomic data is
a challenging task.
Here, we evaluate the usefulness of clustering, data-mining and regression to identify potential new
resistance genes. Three types of analyses were conducted separately over two conditions, tomatoes inoculated
with Phytophthora infestans
and not inoculated tomatoes. Predictions for 10 new resistance genes obtained by
all applied methods were selected as being the most reliable and are therefore reported as potential resistance
Application of different statistical analyses to detect potential resistance genes reliably has shown to
conduct interesting results that improve knowledge on molecular mechanisms of plant resistance to pathogens.