A machine learning framework for the prediction of chromatin folding in <i>Drosophila</i> using epigenetic features.

Rozenwald, Michal B; Galitsyna, Aleksandra A; Sapunov, Grigory V; Khrameeva, Ekaterina E; Gelfand, Mikhail S

A machine learning framework for the prediction of chromatin folding in Drosophila using epigenetic features.

Rozenwald, Michal B; Galitsyna, Aleksandra A; Sapunov, Grigory V; Khrameeva, Ekaterina E; Gelfand, Mikhail S.

Afiliación

Rozenwald MB; Faculty of Computer Science, National Research University Higher School of Economics, Moscow, Russia.
Galitsyna AA; Skolkovo Institute of Science and Technology, Moscow, Russia.
Sapunov GV; Faculty of Computer Science, National Research University Higher School of Economics, Moscow, Russia.
Khrameeva EE; Intento, Inc., Berkeley, CA, USA.
Gelfand MS; Skolkovo Institute of Science and Technology, Moscow, Russia.

PeerJ Comput Sci ; 6: e307, 2020.

Article en En | MEDLINE | ID: mdl-33816958

Palabras clave

Chromatin; DNA folding patterns; Gradient Boosting; Hi-C experiments; Linear Regression; Machine Learning; Recurrent Neural Networks (RNN); Topologically Associating Domains (TADs)

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: PeerJ Comput Sci Año: 2020 Tipo del documento: Article País de afiliación: Rusia Pais de publicación: Estados Unidos

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