Author

Leah Morales

Memorial Sloan Kettering Cancer CenterPrimary ORCID profile (0009-0002-3203-0880 · 2 papers)Unclaimed ORCID profile · follow to get notified of new preprints

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Leah MoralesiD2 papers · Worcester Polytechnic Institute

Biology Disciplines

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Genomics· 1

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Approach:benchmark study· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:variant effect prediction· 1 Assay:whole-exome sequencing· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Chenlian FuiD· 1 shared January Choy· 1 shared Jennifer YuiD· 1 shared Justin JeeiD· 1 shared Madison DarmofaliD· 1 shared Michael F. BergeriD· 1 shared Michele WatersiD· 1 shared Nikolaus SchultziD· 1 shared

Activity by Year

2026-2026 · peak 1/yr

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10 top collaborators · 45 mutual co-author links

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1 shared paperGenomics

Chenlian FuORCID

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1 paper

Recent·Most cited

Generalist large language models complement tailor-made predictors for tumor genomics interpretation

Jennifer Yu, Madison Darmofal, Michele Waters, January Choy, Thinh N. Tran, Chenlian Fu, Leah Morales, U Kaicheng, Ross L. Levine, Nikolaus Schultz, Michael F. Berger, Quaid Morris, Justin Jee

This paper evaluates generalist large language models (LLMs) in tumor genomic interpretation tasks, finding they can complement or outperform task-specific models in some cases.

bioRxiv · May 22, 2026 · PreprintGenomics