Fig 1.
Schematic diagram of genetic algorithm.
Fig 2.
Graphical abstract of the study.
Fig 3.
Architecture of adaptive neuro-fuzzy inference system (ANFIS) model with two input variables and one output variable.
Table 1.
Statistics of multiple linear regression (MLR), stepwise regression (SR), ordinary least squares regression (OLSR), principal component regression (PCR), partial least squares regression (PLSR) and adaptive neuro-fuzzy inference system (ANFIS) for paclitaxel biosynthesis modeling in Corylus avellana cell culture exposed to different concentration of fungal cell extract (FCE-MOD), fungal culture filtrate (FCF-MOD) and fungal cell wall (FCW-MOD) elicitors, either individually or combined treatment with 50 mM methyl-β-cyclodextrin (MBCD).
Fig 4.
Scatter plot of actual data against predicted values of paclitaxel biosynthesis in Corylus avellana cell cultures exposed with different concentration of fungal cell extract (CE), either individually or combined treatment with 50 mM methyl-β-cyclodextrin (MBCD), using adaptive neuro-fuzzy inference system (ANFIS), multiple liner regression (MLR) stepwise regression (SR), ordinary least squares regression (OLSR), principal component regression (PCR) and partial least squares regression (PLSR) models in training subset.
The solid line shows fitted simple regression line on scatter points.
Fig 5.
Scatter plot of actual data against predicted values of paclitaxel biosynthesis in Corylus avellana cell cultures exposed with different concentration of fungal culture filtrate (CF), either individually or combined treatment with 50 mM methyl-β-cyclodextrin (MBCD), using adaptive neuro-fuzzy inference system (ANFIS), multiple liner regression (MLR) stepwise regression (SR), ordinary least squares regression (OLSR), principal component regression (PCR) and partial least squares regression (PLSR) models in training subset.
The solid line shows fitted simple regression line on scatter points.
Fig 6.
Scatter plot of actual data against predicted values of paclitaxel biosynthesis in Corylus avellana cell cultures exposed with different concentration of fungal cell wall (CW), either individually or combined treatment with 50 mM methyl-β-cyclodextrin (MBCD), using adaptive neuro-fuzzy inference system (ANFIS), multiple liner regression (MLR) stepwise regression (SR), ordinary least squares regression (OLSR), principal component regression (PCR) and partial least squares regression (PLSR) models in training subset.
The solid line shows fitted simple regression line on scatter points.
Table 2.
Importance (according to the sensitivity analysis) and optimal levels of the different factors including fungal cell extract (CE; FCE-MOD), culture filtrate (CF; FCF-MOD) and cell wall (CW; FCW-MOD) concentration level, methyl-β-cyclodextrin (MBCD) concentration level, elicitor adding day and harvesting time (day) for achieving maximum paclitaxel biosynthesis in Corylus avellana cell suspension culture (CSC) using adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA).
Table 3.
Paclitaxel biosynthesis in Corylus avellana cell suspension culture (CSC) exposed to optimized fungal elicitor and methyl-β–cyclodextrin concentration levels, fungal elicitor adding day and CSC harvesting time in adaptive neuro-fuzzy inference system (ANFIS) models using genetic algorithm (GA), and predicted paclitaxel biosynthesis via ANFIS-GA.