elmat0 / geneticattribution

Lab of Origin prediction of DNA sequences

GeneticAttribution

Lab of Origin prediction of DNA sequences

Implementation of paper: Deep learning to predict the lab-of-origin of engineered DNA

Nielsen, A and Voigt, C (Voigt Labs)

URL: https://www.nature.com/articles/s41467-018-05378-z.pdf?origin=ppub

Paper results in predictive accuracy of 48%, this model delivers 75.4% on validation set (10%)

NOTE: The dataset used here provided from this URL: https://www.drivendata.org/competitions/63/genetic-engineering-attribution 60,000 DNA sequences with lab of origin as label

WARNING: This model creates a 60391x16048x4 matrix requiring at least 30GB RAM, 64GB recommended minimum

Training done on Intel i9 10-core with dual Nvidia RTX2080rti GPU stack

Training time: 157 mins for 25 epochs

Showcases some good examples of using R to approach a problem:

• Optimize routines by packaging as C++ code
• One-hot encoding of very large matrices
• Finding optimal neural-network parameters
• Loading/Saving objects to disk
• Class weight calculation for unbalanced datasets
• Using customer Keras data generators in R
• Custom scoring