9 projects
SymptomSetModel
A probabilistic, symptom-driven model for estimating rare disease penetrance, expressivity, and prevalence.
CrypticPhenoImpute
Imputes cryptic phenotypes analyzed in Blair et al. into arbitrary clinical datasets.
vlpi
Python implementation of the variational latent phenotype model described in Blair et al..
pyfirth
A very simple, inefficient implemention of Firth-penalized Logistic Regression for rare event data.
PiecewiseBeta
A small class that carries out computations for the Piecewise Beta Distribution.
ProbabilisticPheRS
A semi-supervised probability model for estimating the likelihood that a patient has a rare disease based on their observed symptoms.
VAE-G2P
A fully generative probability model that maps gene-based knowledge directly to disease symptoms, including their reported frequencies.
EmbedOMIM
A generative probability model for embedding Mendelian diseases based on their annotated symptoms (HPO) and their associated frequencies (discrete).
QRankGWAS
Python implementation of the QRank method described in Song et al Bioninformatics 2017.
