Efficient approach for simultaneous estimation of multiple health-promoting effects of foods

The investigation of new food constituents for purposes of disease prevention or health promotion is an area of increasing interest in food science. This paper proposes a new system that allows for simultaneous estimation of the multiple health-promoting effects of food constituents using informatics. The model utilizes expression data of intracellular marker proteins as descriptors that reply to stimulation of a constituent. To estimate three health-promoting effects, namely, cancer cell growth suppression activity, antiviral activity, and antioxidant stress activity, each model was constructed using expression data of marker proteins as input data and health-promoting effects as the output value. When prediction performances of three types of mathematical models constructed by simple, multiple regressions, or artificial neural network (ANN), were compared, the most adequate model was the one constructed using an ANN. There were no statistically significant differences between the actual data and estimated values calculated by the ANN models. This system was able to simultaneously estimate health-promoting effects with reasonable precision from the same expression data of marker proteins. This novel system should prove to be an interesting platform for evaluation of the health-promoting effects of food.