Author

Frederic Tores

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Genomics· 1

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Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:variant effect prediction· 1 Assay:whole-exome sequencing· 1

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Alexandre Alcais· 1 shared Fabienne JABOT-HANINiD· 1 shared Hugo Varet· 1 shared Jean-Philippe Jais· 1 shared

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2016-2016 · peak 1/yr

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4 top collaborators · 6 mutual co-author links

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1 shared paperGenomics

Alexandre Alcais

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1 paper

Recent·Most cited

RFPRED: A RANDOM FOREST APPROACH FOR PREDICTION OF MISSENSE VARIANTS IN HUMAN EXOME

Fabienne JABOT-HANIN, Hugo Varet, Frederic Tores, Alexandre Alcais, Jean-Philippe Jais

RfPred uses random forest to create a meta-score for predicting deleterious missense variants in human exomes, outperforming existing methods and providing pre-calculated scores for all non-synonymous SNPs.

bioRxiv · Jan 20, 2016 · PreprintGenomics