title: Efficient approach for simultaneous estimation of multiple health-promoting effects of foods.
abstract: 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.
authors: Miyazaki Prefectural Industrial Support Foundation, Sadowara-cho, Miyazaki, Japan.
authors: Kiyoko
authors: K
authors: Nagahama
authors: Nozomu
authors: N
authors: Eto
authors: Kunihito
authors: K
authors: Yamamori
authors: Kazuo
authors: K
authors: Nishiyama
authors: Yoichi
authors: Y
authors: Sakakibara
authors: Takako
authors: T
authors: Iwata
authors: Asuka
authors: A
authors: Uchida
authors: Ikuo
authors: I
authors: Yoshihara
authors: Masahito
authors: M
authors: Suiko
mesh_terms: D016588:Anticarcinogenic Agents / Q000032:analysis; D000975:Antioxidants / Q000032:analysis; D000998:Antiviral Agents / Q000032:analysis; D015415:Biomarkers / Q000032:analysis; D045744:Cell Line, Tumor; D004044:Dietary Proteins / Q000032:analysis; D005502:Food; D005504:Food Analysis; D006293:Health Promotion / Q000379:methods; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010940:Plant Proteins / Q000032:analysis; D012044:Regression Analysis
chemical_list: D016588:Anticarcinogenic Agents; D000975:Antioxidants; D000998:Antiviral Agents; D015415:Biomarkers; D004044:Dietary Proteins; D010940:Plant Proteins