A pitfall for machine learning methods aiming to predict across cell types.

Genome Biology
J. SchreiberWilliam Stafford Noble

Abstract

Machine learning models that predict genomic activity are most useful when they make accurate predictions across cell types. Here, we show that when the training and test sets contain the same genomic loci, the resulting model may falsely appear to perform well by effectively memorizing the average activity associated with each locus across the training cell types. We demonstrate this phenomenon in the context of predicting gene expression and chromatin domain boundaries, and we suggest methods to diagnose and avoid the pitfall. We anticipate that, as more data becomes available, future projects will increasingly risk suffering from this issue.

References

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Citations

Jun 10, 2021·Current Opinion in Chemical Biology·Jacob Schreiber, Ritambhara Singh
Aug 12, 2021·Nucleic Acids Research·Alyssa Kramer MorrowNir Yosef

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Methods Mentioned

BETA
RNA-seq

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