Application of XAI to the prediction of CTCF binding sites

The inherent ‘black box’ nature of deep learning models has hindered their widespread adoption in certain fields, as they provide limited transparency into the reasoning behind their predictions. In the last years, Explainable Artificial Intelligence (XAI) techniques have proven to be effective not only in prediction itself but also in the extraction of meaningful knowledge from deep learning models by means of feature interpretation. In this study, Local Interpretable Model-agnostic Explanations are applied to the prediction of CTCF binding sites, a common task in the field of genomics. Good prediction performances and inferred explanations are obtained that highlight the most informative features that contribute to predictions such as chromatin accessibility and cis-regulatory elements which align well with previously reported data. This work represents a proof of concept showing that XAI are suitable for the extraction of molecular insights from complex biological problems like CTCF binding prediction. • Application of machine learning techniques to the CTCF binding sites problem. • Testing generalization capabilities of machine learning models obtained on different tissues. • Applying XAI to DL models trained on genomic data in order to tackle the CTCF binding site problem. • Improving the interpretability and utility of DL models in analyzing biological data.