Inspiration
The inspiration behind GeneLevel sprang from the realization that the one-size-fits-all dietary guidelines fail to account for individual genetic variations affecting nutrient metabolism. This gap in personalized nutrition sparked our ambition to tailor dietary plans right down to the genetic level, ensuring everyone can eat precisely what their body needs for optimal health.
What it does
GeneLevel revolutionizes dietary planning by integrating advanced genomic data analysis with machine learning to identify upregulated or downregulated marker genes indicative of an individual's unique nutritional requirements. Our platform categorizes users into optimal dietary plans, such as high-fat or low-fat diets, based on their blood mRNA expression data and questionnaire responses, providing personalized menu plans that promote physical and cognitive well-being.
How we built it
We harnessed a combination of Gene Set Enrichment Analysis (GSEA) and machine learning algorithms to sift through genomic data, identifying key nutritional markers. The frontend was crafted with modern web technologies to make genomic data accessible, while the backend ML models were trained on rich datasets to ensure accurate dietary classifications.
Challenges we ran into
- Data Collection: Amassing a comprehensive and varied dataset of blood mRNA expression data was a significant hurdle, critical for the model's training and validation phases.
- Accuracy of the ML Model: Achieving high accuracy and reliability in our machine learning predictions was challenging, necessitating continuous iterations and model optimizations.
Accomplishments that we're proud of
- FrontEnd Programming: We're particularly proud of developing a user-friendly interface that demystifies genomic data, making personalized nutrition accessible to everyone.
What we learned
Throughout this project, we delved deep into the intricacies of genomic data and its impact on nutrition. We learned the importance of data quality, the challenges of interpreting complex biological information, and the potential of machine learning to bridge the gap between genetics and dietetics.
What's next for GeneLevel - the Next-Generation of Personalized Nutrition
- Fine-tuning the ML model: We aim to enhance the model's predictive accuracy by incorporating more diverse mRNA expression data, especially from studies focusing on obesity.
- Expanding Dietary Categories: Beyond high-fat and low-fat diets, we plan to introduce more nuanced dietary categories, allowing for even more personalized nutrition plans tailored to individual genetic profiles and health goals.
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