In this episode, we hear from Jacob Schreiber about his algorithm,

Avocado.

Avocado uses deep tensor factorization to break a three-dimensional tensor of

epigenomic data into three orthogonal dimensions corresponding to cell types,assay types, and genomic loci. Avocado can extract a low-dimensional,information-rich latent representation from the wealth of experimental datafrom projects like the Roadmap Epigenomics Consortium and ENCODE. Thisrepresentation allows you to impute genome-wide epigenomics experiments thathave not yet been performed.

Jacob also talks about a pitfall he discovered when trying to predict gene

expression from a mix of genomic and epigenomic data. As you increase thecomplexity of a machine learning model, its performance may be increasing forthe wrong reason: instead of learning something biologically interesting, yourmodel may simply be memorizing the average gene expression for that geneacross your training cell types using the nucleotide sequence.

Links:

• Avocado on GitHub
• Multi-scale deep tensor factorization learns a latent representation of the human epigenome (Jacob Schreiber, Timothy Durham, Jeffrey Bilmes, William Stafford Noble)
• Completing the ENCODE3 compendium yields accurate imputations across a variety of assays and human biosamples (Jacob Schreiber, Jeffrey Bilmes, William Noble)
• A pitfall for machine learning methods aiming to predict across cell types (Jacob Schreiber, Ritambhara Singh, Jeffrey Bilmes, William Stafford Noble)