1. Microbiome Research
1.1. Food - Microbiome
1.2. Microbiome - Oncology
1.3. Microbiome- Allergies
1.4. Microbiome-diabetes
1.5. Microbiome- Cardiac
2. Precision Nutrition Datasets
2.1. Micronutrient and Macronutrient- core datasets
2.1.1. Government Datasets
2.1.1.1. Australia
2.1.1.2. FNDDS
2.1.1.3. Legacy
2.1.1.4. Branded Foods
2.1.2. Dataset Augmentation
2.1.2.1. Nova Score
2.1.2.2. allergens
2.1.2.3. FODMAP
2.1.2.3.1. low
2.1.2.3.2. med
2.1.2.3.3. high
2.1.2.4. carotenoids
2.1.2.5. Flavonoids
2.1.2.6. Microbiome explainer list
2.1.2.7. GI for a combination dish
2.1.2.7.1. to be followed up with Ameera
2.1.3. Scoring
2.1.3.1. Nutrient Traffic Light
2.1.3.1.1. Indicator
2.1.3.1.2. RDA
3. Challenges
3.1. FODMAP
3.1.1. outsource to annotator
3.2. Glycaemic Index
3.2.1. match and apply computation
3.3. Fiber
3.3.1. soluble
3.3.2. insoluble
4. Worflows
4.1. Automation of Scoring ( line by line)
4.2. Consolidation of the food lists
5. Patient
6. Recipe and Food Tags
6.1. Vegan
6.2. Lacto-OVO
6.3. Halal
6.4. Pescetarian
6.5. Keto
6.6. low/hi tags
6.6.1. calorie
6.6.1.1. Low
6.6.1.2. Medium
6.6.1.3. high
6.6.2. fat
6.6.2.1. healthy
6.6.2.2. unhealthy
6.6.3. protein
6.6.3.1. Low
6.6.3.2. Medium
6.6.3.3. high
6.6.4. fiber
6.6.4.1. soluble
6.6.4.2. insoluble
6.6.5. carbohydrate
6.6.6. fresh fruit and vegetable
6.7. Health Impact
6.7.1. Gut Healthy
6.7.2. Glycaemic Control
6.7.3. Heart Health
6.7.4. Low Sodium