Prediction and optimization of indirect shoot regeneration of Passiflora caerulea using machine learning and optimization algorithms.

ABSTRACT:

Background

Optimization of indirect shoot regeneration protocols is one of the key prerequisites for the development of Agrobacterium-mediated genetic transformation and/or genome editing in Passiflora caerulea. Comprehensive knowledge of indirect shoot regeneration and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms.

Materials and methods

In the present investigation, the indirect shoot regeneration responses (i.e., de novo shoot regeneration rate, the number of de novo shoots, and length of de novo shoots) of P. caerulea were predicted based on different types and concentrations of PGRs (i.e., TDZ, BAP, PUT, KIN, and IBA) as well as callus types (i.e., callus derived from different explants includi

SUBMITTER: Jafari M 

PROVIDER: S-EPMC10394921 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature