Improving knowledge of plant tissue culture and media formulation by neurofuzzy logic: a practical case of data mining using apricot databases.

Plant tissue growth can be regulated and controlled via culture media composition. A number of different laborious and time-consuming approaches have been used to attempt development of optimized media for a wide range of species and applications. However, plant tissue culture is a very complex task, and the identification of the influences of process factors such as mineral nutrients or plant growth regulators on a wide spectrum of growth responses cannot always well comprehended. This study employs a new approach, data mining, to uncover and integrate knowledge hidden in multiple data from plant tissue culture media formulations using apricot micropropagation databases as an example. Neurofuzzy logic technology made it possible to identify relationships among several factors (cultivars, mineral nutrients and plant growth regulators) and growth parameters (shoots number, shoots length and productivity), extracting biologically useful information from each database and combining them to create a model. The IF-THEN rule sets generated by neurofuzzy logic were completely in agreement with previous findings based on statistical analysis, but advantageously generated understandable and reusable knowledge that can be applied in future plant tissue culture media optimization.