#32 Deep tensor factorization and a pitfall for machine learning methods with Jacob Schreiber

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 data

from projects like the Roadmap Epigenomics Consortium and ENCODE. This

representation allows you to impute genome-wide epigenomics experiments that

have 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 the

complexity of a machine learning model, its performance may be increasing for

the wrong reason: instead of learning something biologically interesting, your

model may simply be memorizing the average gene expression for that gene

across 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)

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