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

Genome Biology

Ritambhara Singh¹  William Stafford Noble²  Jeffrey Bilmes²  Jacob Schreiber² 

[1] Department of Genome Science, University of Washington;Paul G. Allen School of Computer Science & Engineering, University of Washington;

关键词: Machine learning;    Epigenomics;    Genomics;   

DOI  :  10.1186/s13059-020-02177-y

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown