Inferring binding specificities of human transcription factors with the wisdom of crowds.

ABSTRACT: DNA motif discovery and, particularly, computational modeling of transcription factor binding motifs, has been a mecca of algorithmic bioinformatics for several decades. Here, we report the results of the largest open community challenge in Inferring BInding Specificities (IBIS), where participants all over the world were invited to construct binding specificity models from multi-assay experimental data for poorly studied human transcription factors. The submissions were rigorously tested against a rich held-out dataset. Benchmarking demonstrated a consistent advantage of properly designed deep learning models over traditional positional weight matrices and other machine learning methods. Yet, the positional weight matrices displayed a surprisingly strong performance out of the box, being

SUBMITTER: Gryzunov N 

PROVIDER: S-EPMC12667932 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature