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Description
SIGNIFICANCE
As a whole, the impact of this work will be to (a) accelerate knowledge discovery and applications around health and nutrition by creating the necessary infrastructure (databases, algorithms, software packages, benchmarks), (b) demonstrate that an end-to-end diet recommendation system is possible and create a first proof-of-concept implementation that can be refined and adopted in the future, which in turn will add in our tool arsenal for a healthier society through better food choices.
GOALS
The specific aims are (a) create the necessary components for a diet recommendation system (aim 1), (b) integrate these components to a DRS system and evaluate its performance, including explainability (aim 2), (c) fine-tune and design the next generation DRS for Y3 (aim 3).
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Team

Ilias Tagkopoulos
Principal Investigator

Danielle Lemay
Co Principal Investigator

Zhaodan Kong
Co Principal Investigator

Xin Liu
Co Principal Investigator
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Publications

The Development of the Davis Food Glycopedia—A Glycan Encyclopedia of Food

Chardonnay Marc As a New Model for Upcycled Co-products in the Food Industry: Concentration of Diverse Natural Products Chemistry for Consumer Health and Sensory Benefits

Algorithmic Lifestyle Optimization

An Early Prediction Model for Canine Chronic Kidney Disease Based on Routine Clinical Laboratory Tests

A Computational Algorithm to Assess the Physiochemical Determinants of T Cell Receptor Dissociation Kinetics

Understanding the Formation and Mechanism of Anticipatory Responses in Escherichia Coli

Knowledge Integration and Decision Support for Accelerated Discovery of Antibiotic Resistance Genes

Special Report: AI Institute for Next Generation Food Systems (AIFS)

Constrained Reinforcement Learning for Autonomous Farming: Challenges and Opportunities

Dietary Intake of Monosaccharides From Foods Is Associated with Characteristics of the Gut Microbiota and Gastrointestinal Inflammation in Healthy US Adults

Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance
