Author

Gregory J. Riely

Memorial Sloan Kettering Cancer CenterORCID 0000-0001-5357-4313Unclaimed ORCID profile · follow to get notified of new preprints

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

Frequent Co-Authors

Anisha LuthraiD· 1 shared Chris FongiD· 1 shared Debyani ChakravartyiD· 1 shared Justin JeeiD· 1 shared Karl PichottaiD· 1 shared Marc LadanyiiD· 1 shared Michael Fabian BergeriD· 1 shared Michele WatersiD· 1 shared

Activity by Year

2024-2024 · peak 1/yr

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10 top collaborators · 45 mutual co-author links

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1 shared paperGenomics

Anisha LuthraORCID

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1 paper

Recent·Most cited

Machine learning predictions improve identification of real-world cancer driver mutations

Thinh Ngoc Tran, Chris Fong, Karl Pichotta, Anisha Luthra, Ronglai Shen, Yuan Chen, Michele Waters, Susie Kim, Michael Fabian Berger, Gregory J. Riely, Marc Ladanyi, Debyani Chakravarty, Nikolaus D. Schultz, Justin Jee

The paper uses machine learning to identify cancer driver mutations, finding that methods incorporating protein structure or functional genomic data outperform those using only evolutionary data, and validates their predictions using real-world patient cohorts.

bioRxiv · Mar 31, 2024 · PreprintGenomics