Author

May Taha

Centre National de la Recherche ScientifiqueORCID 0000-0001-9592-449XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Assay:ChIP-seq· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Approach:statistical model· 1

Frequent Co-Authors

Charles‐Henri LecellieriD· 1 shared Chloé BessièreiD· 1 shared Florent PetitpreziD· 1 shared Jean‐Michel MariniD· 1 shared Jimmy VandeliD· 1 shared Laurent BréhéliniD· 1 shared Sophie LèbreiD· 1 shared

Activity by Year

2018-2018 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Charles‐Henri LecellierORCID

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1 paper

Recent·Most cited

Gene nucleotide composition accurately predicts expression and is linked to topological chromatin domains

Chloé Bessière, May Taha, Florent Petitprez, Jimmy Vandel, Jean‐Michel Marin, Laurent Bréhélin, Sophie Lèbre, Charles‐Henri Lecellier

The paper develops a sequence-based method to predict gene expression accurately, showing that nucleotide composition and regulatory region contributions are key factors.

PLoS Computational Biology · Jan 2, 2018Peer-reviewedBioinformatics