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The perils of interaction prediction

View ORCID ProfileWeiguang Mao, Dennis Kostka, Maria Chikina
doi: https://doi.org/10.1101/435065
Weiguang Mao
1Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
2Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology, Pittsburgh, PA, USA
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  • ORCID record for Weiguang Mao
Dennis Kostka
1Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
2Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology, Pittsburgh, PA, USA
3Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
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Maria Chikina
1Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
2Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology, Pittsburgh, PA, USA
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Abstract

The availability of genome-wide maps of enhancer-promoter interactions (EPIs) has made it possible to use machine learning approaches to extract and interpret features that determine these interactions in different biological contexts. Multiple methods have claimed to accomplish the task of predicting enhancer-promoter interactions based on corresponding genomic features, but this problem is actually still far from being solved. In our analysis, we show that individual enhancer and promoter regions have widely different marginal interaction probabilities, e.g. propensities, which can lead to overfitting and memorization when random cross-validation is employed. Further even when a proper cross-validation scheme is adopted, a simple propensity-based model can still achieve a competitive performance without capturing any information about the EPI mechanism.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 05, 2018.
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The perils of interaction prediction
Weiguang Mao, Dennis Kostka, Maria Chikina
bioRxiv 435065; doi: https://doi.org/10.1101/435065
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The perils of interaction prediction
Weiguang Mao, Dennis Kostka, Maria Chikina
bioRxiv 435065; doi: https://doi.org/10.1101/435065

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