Histone Mark Modifications (HMs) are crucial actors in gene regulation, as they actively remodel chromatin to modulate transcriptional activity: aberrant combinatorial patterns of HMs have been connected with several diseases, including cancer. HMs are, however, reversible modifications: understanding their role in disease would allow the design of 'epigenetic drugs' for specific, non-invasive treatments. Standard statistical techniques were not entirely successful in extracting representative features from raw HM signals over gene locations. On the other hand, deep learning approaches allow for effective automatic feature extraction, but at the expense of model interpretation.

Accurate and highly interpretable prediction of gene expression from histone modifications

Frasca, Fabrizio;Matteucci, Matteo;Leone, Michele;Masseroli, Marco
2022-01-01

Abstract

Histone Mark Modifications (HMs) are crucial actors in gene regulation, as they actively remodel chromatin to modulate transcriptional activity: aberrant combinatorial patterns of HMs have been connected with several diseases, including cancer. HMs are, however, reversible modifications: understanding their role in disease would allow the design of 'epigenetic drugs' for specific, non-invasive treatments. Standard statistical techniques were not entirely successful in extracting representative features from raw HM signals over gene locations. On the other hand, deep learning approaches allow for effective automatic feature extraction, but at the expense of model interpretation.
Epigenetics
Gene expression regulation
Histone modifications
Interpretability
Chromatin
Gene Expression
Protein Processing, Post-Translational
Histone Code
Histones
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1224736
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