Neurofuzzy logic predicts a fine-tuning metabolic reprogramming on elicited PCSCs guided by salicylic acid.

Novel approaches to the characterization of medicinal plants as biofactories have lately increased in the field of biotechnology. In this work, a multifaceted approach based on plant tissue culture, metabolomics, and machine learning was applied to decipher and further characterize the biosynthesis of phenolic compounds by eliciting cell suspension cultures from medicinal plants belonging to the subgenus. The application of untargeted metabolomics provided a total of 460 phenolic compounds.

The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in Medicinal Plants (Genus ).

Phenolic compounds constitute an important family of natural bioactive compounds responsible for the medicinal properties attributed to plants (genus , Crassulaceae), but their production by these medicinal plants has not been characterized to date. In this work, a combinatorial approach including plant tissue culture, untargeted metabolomics, and machine learning is proposed to unravel the critical factors behind the biosynthesis of phenolic compounds in these species. The untargeted metabolomics revealed 485 annotated compounds that were produced by three species cultured in vitro in a genotype and organ-dependent manner.

Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant sp. Cultured .

Plant nutrition is a crucial factor that is usually underestimated when designing plant culture protocols of unexploited plants. As a complex multifactorial process, the study of nutritional imbalances requires the use of time-consuming experimental designs and appropriate statistical and multiple regression analysis for the determination of critical parameters, whose results may be difficult to interpret when the number of variables is large. The use of machine learning (ML) supposes a cutting-edge approach to investigate multifactorial processes, with the aim of detecting non-linear relationships and critical factors affecting a determined response and their concealed interactions.

From Ethnomedicine to Plant Biotechnology and Machine Learning: The Valorization of the Medicinal Plant sp.

The subgenus includes about 25 plant species native to Madagascar, and is widely used in traditional medicine worldwide. Different formulations from have been employed for the treatment of several ailments, including infections, gynecological disorders, and chronic diseases, such as diabetes, neurological and neoplastic diseases. Two major families of secondary metabolites have been reported as responsible for these bioactivities: phenolic compounds and bufadienolides.

Machine Learning Technology Reveals the Concealed Interactions of Phytohormones on Medicinal Plant In Vitro Organogenesis.

Organogenesis constitutes the biological feature driving plant in vitro regeneration, in which the role of plant hormones is crucial. The use of machine learning (ML) technology stands out as a novel approach to characterize the combined role of two phytohormones, the auxin indoleacetic acid (IAA) and the cytokinin 6-benzylaminopurine (BAP), on the in vitro organogenesis of unexploited medicinal plants from the subgenus. The predictive model generated by neurofuzzy logic, a combination of artificial neural networks (ANNs) and fuzzy logic algorithms, was able to reveal the critical factors affecting such multifactorial process over the experimental dataset collected.

Combining Medicinal Plant In Vitro Culture with Machine Learning Technologies for Maximizing the Production of Phenolic Compounds.

We combined machine learning and plant in vitro culture methodologies as a novel approach for unraveling the phytochemical potential of unexploited medicinal plants In order to induce phenolic compound biosynthesis, the in vitro culture of three different species of under nutritional stress was established. To optimize phenolic extraction, four solvents with different MeOH proportions were used, and total phenolic content (TPC), flavonoid content (FC) and radical-scavenging activity (RSA) were determined. All results were subjected to data modeling with the application of artificial neural networks to provide insight into the significant factors that influence such multifactorial processes.