Author

Federico Divina

Universidad Pablo de OlavideORCID 0000-0002-0964-9506Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:ChIP-seq· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Francisco Gómez-VelaiD· 2 shared Miguel García-TorresiD· 2 shared Pedro Manuel Martínez‐GarcíaiD· 2 shared Felipe Cortés‐LedesmaiD· 1 shared Irene Delgado‐SainziD· 1 shared José Terrón-BautistaiD· 1 shared Thomas VanhaereniD· 1 shared Wim VanhoofiD· 1 shared

Activity by Year

2020-2021 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 22 mutual co-author links

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2 shared papersBioinformatics

Francisco Gómez-VelaORCID

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2 papers

Recent·Most cited

Genome-wide prediction of topoisomerase IIβ binding by architectural factors and chromatin accessibility

Pedro Manuel Martínez‐García, Miguel García-Torres, Federico Divina, José Terrón-Bautista, Irene Delgado‐Sainz, Francisco Gómez-Vela, Felipe Cortés‐Ledesma

This paper predicts TOP2B binding sites using machine learning, showing that chromatin accessibility and architectural factors are sufficient for accurate predictions.

PLoS Computational Biology · Jan 19, 2021Peer-reviewedBioinformatics

A comparative study of supervised machine learning algorithms for the prediction of long-range chromatin interactions

Thomas Vanhaeren, Federico Divina, Miguel García-Torres, Francisco Gómez-Vela, Wim Vanhoof, Pedro Manuel Martínez‐García

This study compares machine learning algorithms for predicting long-range chromatin interactions using 1D sequencing data, finding gradient boosting to be the most accurate.

Genes · Aug 24, 2020Peer-reviewedBioinformatics