Classic machine learning on top of multiple position weight matrices improves genomic prediction of transcription factor binding sites (opens in new tab)

Motivation: DNA motifs recognised by transcription factors are typically represented as position weight matrices (PWMs), assuming independent contributions of individual nucleotides to protein binding specificity. Many alternative models accounting for correlations of positional contributions have been introduced in the past decades. However, performance gains have generally not out-weighed the advantages of simplicity, interpretability, and practical applicability of PWMs with the well-estab...