Author

Ragousandirane Radjasandirane

InsermORCID 0000-0002-6311-6794Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:GWAS summary statistics· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:protein sequence· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Alexandre G. de BreverniD· 2 shared Jean‐Christophe GellyiD· 2 shared Julien DiharceiD· 2 shared Gabriel CretiniD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

4 top collaborators · 6 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Alexandre G. de BrevernORCID

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2 papers

Recent·Most cited

PATHOS: Predicting Variant Pathogenicity by Combining Protein Language Models and Biological Features

Ragousandirane Radjasandirane, Gabriel Cretin, Julien Diharce, Alexandre G. de Brevern, Jean‐Christophe Gelly

PATHOS predicts variant pathogenicity by integrating protein language models and biological features, achieving high accuracy on clinical and ClinVar datasets.

Artificial Intelligence in the Life Sciences · Feb 21, 2026Peer-reviewedGenetic & Genomic Medicine

Assessment of variant effect predictors unveils variants difficulty as a critical performance indicator

Ragousandirane Radjasandirane, Julien Diharce, Jean‐Christophe Gelly, Alexandre G. de Brevern

This study evaluates 65 variant effect predictors, finding AlphaMissense performs best and variants are classified as easy, moderate, or hard to predict based on structural and functional information.

Genomics · Mar 22, 2025Peer-reviewedBioinformatics