I’m currently working on a few textbook projects aided by AI Astra model:
1. Learning from waves to detect localized effects. This goes into electrocardiogram data.
2. Spherical harmonics. Using it for regression and modeling multiscale data.
3. Mind reading. Using world foundation models to read minds across time and space.
I’ll post updates as I go.
Interpreting rare private mutations is one of the next challenges in human genetics. Some ideas for interpreting them include downstream studies with functional assays, e.g. see Robert Plenge’s keynote lecture at the Variant to Function symposium dissecting the functional consequences of TYK2 ultra rare variants.
We built an AI agent that integrates literature to interpret the consequences of newly identified variants from whole-genome sequencing data.
Spherical harmonics are a family of mathematical functions that describe patterns on the surface of a sphere. They are widely used in physics, computer graphics, and geomathematics.
Here, we present new approaches to detect localized genetic effects using spherical harmonics. The write-up is on bioRxiv – we have simulations and apply the framework to FinnGen coding dataset.
If you want to read how spherical harmonics are used for testing against the null of multivariate normality, please see the write-up below.
