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Non-linear Genetic Effects for Complex Traits

Jupyter Notebook 56.11% HTML 39.89% Python 3.05% R 0.96%
bioinformatics epistasis genetics machine-learning

cs-m226-project's Introduction

CS M226: Machine Learning for Bioinformatics

This is the repo documenting the project Non-linear Genetic Effects for Complex Traits.

Group Members:

Yu-Wei Chen, Zhengtong Liu, Jacob Wallin

Main work:

  • Study Variational Inference for the Polygenetic Risk Scores models (Bayesian Networks, Spike and Slab Priors, Evidence Lower Bound, etc.);
  • Modify the Biologically Annotated Neural Networks (BANNs) architecture to capture non-linear genetic effects within genes;
  • Compare BANNs with Variational Inference of Polygenic Risk Scores;
  • Apply Polygenetic Risk Scores models to genotype data from 1000 Genomes Project and simulated phenotype (experience with PLINK).

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