Towards a future free of disease (& looking for collaborators)
Summary
Ammon Bartram and Michael Poon are launching Tabula, a bioinformatics company aimed at improving complex disease diagnosis through machine learning, addressing the gap between genetic heritability and disease prediction.
Why It Matters
This initiative is significant as it seeks to leverage advanced machine learning techniques to enhance our understanding of genetic diseases, which could lead to breakthroughs in personalized medicine. By addressing the limitations of current genetic models, Tabula aims to pave the way for more effective disease prediction and treatment strategies, potentially transforming healthcare outcomes.
Key Takeaways
- Current genetic models fail to explain a significant portion of disease heritability, necessitating new approaches.
- Machine learning is seen as a key tool to bridge the gap in understanding complex genetic diseases.
- Novel ML architectures must incorporate unlabeled genomic data and expert research to improve predictive accuracy.
- The integration of epigenetic data is crucial for developing comprehensive disease models.
- Collaborations and feedback are sought to refine hypotheses and drive innovation in bioinformatics.
Hacker Newsnew | past | comments | ask | show | jobs | submitloginTowards a future free of disease (& looking for collaborators)7 points by ammon 10 months ago | hide | past | favorite | 1 commentHello HN! Michael Poon and I are starting a bioinformatics company (Tabula) to improve complex disease diagnosis. Weâre just getting started, and weâd love feedback (harden our hypotheses), new ideas, and anyone interested in collaborating.Many of the most devastating human diseases are heritable. Whether an individual develops schizophrenia, obesity, diabetes, or autism depends more on their genes (and epi-genome) than it does on any other factor. However, current genetic models do not explain all of this heritability. Twin studies show that schizophrenia, for example, is 80% heritable (over a broad cohort of Americans), but our best genetic model only explains ~9% of variance in cases. I selected a dramatic example here (models for other diseases perform better). Still, the gap between heritability and prediction stands in the way of personalized genetic medicine. We are launching Tabula Bio to close this gap. We have a three-part thesis on how to approach this.1. The path forward is machine learning. The human genome is staggeringly complex. In the 20 years since the Human Gnome Project, much progress has been made, but we are still entirely short of a mechanistic, bottom-up model that would allow anything like disease prediction. Instead, we have to rely on statistical mod...
