Abstract：
: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.
journal_name
J Plant Physioljournal_title
Journal of plant physiologyauthors
Gago J,Pérez-Tornero O,Landín M,Burgos L,Gallego PPdoi
10.1016/j.jplph.2011.04.008subject
Has Abstractpub_date
2011-10-15 00:00:00pages
1858-65issue
15eissn
0176-1617issn
1618-1328pii
S0176-1617(11)00251-3journal_volume
168pub_type
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