<ul><li>A new benchmarking paradigm called Leave-one-chromosome-out (LOCO) has been proposed for deep learning based prediction of enhancer-promoter interactions (EPI).</li><li>Traditional methods randomly split the dataset into training and testing subsets, leading to performance overestimation due to information leakage.</li><li>The LOCO cross-validation approach demonstrates that a deep learning algorithm's performance drops drastically, highlighting the overestimation of performance in random-splitting settings.</li><li>A novel hybrid deep neural network that combines k-mer features of the nucleotide sequence is proposed, showing significantly better performance in the LOCO setting.</li></ul>

LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions

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