AI-driven Personalized Dietary Predictor Design Study

Many of the technologies we routinely use already, such as search engines (e.g., Google), streaming music services (e.g., YouTube Music), video services (e.g., Netflix), and online shopping sites (e.g., Amazon), are already identifying the personal characteristics of humans and predicting what the personal want, along with the vast amount of data from others. The power of this big data is changing many areas of human life.

Is there anything as important as eating as a subject of research to maintain a healthy life? The human body consists of what we eat, but how well do we know what to eat? Or how well do you know what's right for you?

The concept of precision health care is being strengthened, with emphasis on 4P—prediction, prevention, personalization and participation. Already, the market is adopting big data and artificial intelligence technology to propose and supply food tailored to individuals. Now these services need to be verified through academic research and strong scientific evidences.

We are about to study the Agro-Nutrient-Health association through the extensive data extracted from literature, public databases, and PubMed using Natural Language Processing (NLP). The efforts to understand the food complexity (foodome) and the individual's genome, epi-genome, transcriptome, proteome, metabolome, microbiome, and exposome are undergoing. These big data enable us to identify the pharmacology of foodome and the physiological characteristics of individuals and distinguish between responders and non-responders to specific nutrients through machine learning and artificial intelligence (AI).

The AI-driven personalized dietary predictor we are trying to implement can be applied to the development of personalized sustainable agro-medical products—medicine, oriental medicine, supplements, cosmetic, diet, smart farms, etc. We are collaborating with leading bio-tech companies and working hard to raise money for the research to design the AI-driven personalized dietary predictor.

We are always working on future-oriented keywords, agriculture, planet, environment, anthropocene, sustainability, food, bio, convergence, health, personalization, big data, AI, etc. We would like to share relevant social issues and trends with various people through the hosting of symposiums and contribute to the popularization of related disciplines.

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Grant (중견)

"뇌건강 foodome-physiome-exposome 지식을 기반으로 하는 개인 맞춤 브레인 푸드 머신러닝 예측 모델 개발" 연구가 한국연구재단 중견연구사업에 선정되었습니다. (2021.03.01 - 2023.02.28) (주)디이프와 공동으로 연구합니다.