Abstract: The disclosed systems and methods provide a systematic approach to analyzing an individual's lifestyle factors (e.g., foods consumed by the individual) that contribute to the individual's current or potential for disease, and taking further action based on that analysis. One example embodiment is a machine learning system that includes a food composition layer, chemical compounds layer, and disease layer. The food composition layer provides representations of chemical compounds of foods consumed or to be consumed by the individual. The chemical compounds layer is coupled to the food composition layer by links and filters the representations of the chemical compounds based on genetic or metabolic information of the individual, resulting in representations of personalized filtered chemical compounds.

Abstract: The disclosed systems and methods provide a systematic approach to analyzing an individual's lifestyle factors (e.g., foods consumed by the individual) that contribute to the individual's current or potential for disease, and taking further action based on that analysis. One example embodiment is a machine learning system that includes a food composition layer, chemical compounds layer, and disease layer. The food composition layer provides representations of chemical compounds of foods consumed or to be consumed by the individual. The chemical compounds layer is coupled to the food composition layer by links and filters the representations of the chemical compounds based on genetic or metabolic information of the individual, resulting in representations of personalized filtered chemical compounds.

Abstract: Systems and methods for identifying a degree of food processing based on food nutrient content are provided. Given in a nutrient profile for a food that includes nutrient content data for the food, a vector of probabilities is generated in which each probability of the vector represents a probability associated with a processing category for the food. A food processing score is determined based on the vector of probabilities and displayed. An individual food score can also be determined based on a plurality of food processing scores. The individual food processing score can be weight-based or calorie-based.

Abstract: The disclosed systems and methods provide a systematic approach to analyzing an individual's lifestyle factors (e.g., foods consumed by the individual) that contribute to the individual's current or potential for disease, and taking further action based on that analysis. One example embodiment is a machine learning system that includes a food composition layer, chemical compounds layer, and disease layer. The food composition layer provides representations of chemical compounds of foods consumed or to be consumed by the individual. The chemical compounds layer is coupled to the food composition layer by links and filters the representations of the chemical compounds based on genetic or metabolic information of the individual, resulting in representations of personalized filtered chemical compounds.

Abstract: Methods and systems for filtering data in a protein-protein interaction network are provided, which can be used to identify potential food-drug interactions. A method of filtering data in protein-protein interaction network includes mapping proteins associated with a plurality of chemicals of a first type (e.g drugs) and proteins associated with one or more chemicals of a second type (e.g., foods). The method further includes determining proximities of proteins associated with the plurality of chemicals of the first type and proteins associated with the one or more chemicals of the second type and generating a reduced dataset of proteins within the protein-protein interaction network. The reduced dataset includes proteins associated with a subset of the plurality of chemicals of the first type based on the determined proximities.

Abstract: The disclosed systems and methods provide a systematic approach to analyzing an individual's lifestyle factors (e.g., foods consumed by the individual) that contribute to the individual's current or potential for disease, and taking further action based on that analysis. One example embodiment is a machine learning system that includes a food composition layer, chemical compounds layer, and disease layer. The food composition layer provides representations of chemical compounds of foods consumed or to be consumed by the individual. The chemical compounds layer is coupled to the food composition layer by links and filters the representations of the chemical compounds based on genetic or metabolic information of the individual, resulting in representations of personalized filtered chemical compounds.

Abstract: The disclosed systems and methods provide a systematic approach to analyzing an individual's lifestyle factors (e.g., foods consumed by the individual) that contribute to the individual's current or potential for disease, and taking further action based on that analysis. One example embodiment is a machine learning system that includes a food composition layer, chemical compounds layer, and disease layer. The food composition layer provides representations of chemical compounds of foods consumed or to be consumed by the individual. The chemical compounds layer is coupled to the food composition layer by links and filters the representations of the chemical compounds based on genetic or metabolic information of the individual, resulting in representations of personalized filtered chemical compounds.