Abstract: A method of predicting facial biological age of an individual is provided comprising a computer receiving methylation data and facial image data describing an individual. The method also comprises the computer receiving survey data provided by the individual and the computer applying an age predictor clock model to at least the received data to predict a facial biological age of the individual. The method also comprises the computer identifying causal methylation markers in the methylation data. The method also comprises the computer generating a personalized report for the individual describing the predicted facial biological age based on methylation markers, and the report further describing methylation markers causal to facial aging, the markers identified at least in the data. The personalized report further contains facial ageotypes from face image data. The age predictor clock model is trained and validated on reference population data stored in the reference population database.

Abstract: A system for computing risk predisposition to gestational hypertensive disorders for an individual woman is provided. The system comprises a computer and an application stored in the computer that when executed computes, based on received data, risk predisposition to gestational hypertensive disorders for a female. The system also calculates, based at least on the computed risk predisposition, calories, and macro-and micro-nutrient needs for the female. The system also generates, based on the calculations, dietary recommendations, foods, and recipes for the female. The system further receives feedback from a plurality of females regarding liking/disliking of at least previous dietary recommendations comprising foods and recipes and receives further feedback regarding adverse reactions comprising at least one of morning sickness and nausea. The system further receives additional data comprising at least one of pregnancy complications, blood pressure, and glucose levels.

Abstract: A system for computing risk predisposition to gestational diabetes mellitus for an individual woman is provided. The system computes, based on received data, risk predisposition to gestational diabetes for a female. The system also calculates, based at least on the computed risk predisposition, calories and macro- and micro-nutrient needs for the female. The system also generates, based on the calculations, dietary recommendations, foods, and recipes for the female. The system further receives feedback from a plurality of female humans regarding liking/disliking and adverse reactions comprising at least one of morning sickness and nausea. The system further receives additional data comprising at least one of pregnancy complications, blood pressure, and glucose levels. The system further improves, based at least on the feedback and the received additional data, and via at least a machine learning methodology, assessment of risk predisposition to gestational diabetes for the plurality of female humans.

Abstract: A system for optimizing nutrition for women during stages of preconception, pregnancy, and lactation/postpartum is provided. The system comprises a computer and a calculator engine application executing on the computer that receives a request from a user device to create a dietary program for a woman. The application receives data describing the woman, the data including a current stage, the stage comprising one of preconception, pregnancy, and lactation/postpartum. The application compares the data to reference population data. The application also extracts material from a knowledge base describing at least one of levels of micro- and macro-nutrients at each stage and effects of various factors on levels of micro- and macro-nutrients at each pregnancy stage.