Author

Arshi Arora

Memorial Sloan Kettering Cancer CenterORCID 0000-0002-4040-1787Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Arshi Arora1 paper

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:statistical model· 2 Assay:whole-exome sequencing· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Ronglai SheniD· 2 shared Venkatraman SeshaniD· 2 shared Adam B. OlsheniD· 1 shared Allison ReineriD· 1 shared Caroline E. KostrzewaiD· 1 shared Colin B. BeggiD· 1 shared Eva HernandoiD· 1 shared InterMEL· 1 shared

Activity by Year

2020-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersGenomics

Ronglai ShenORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Improving Accuracy of Somatic Mutation Profiling in Large Epidemiologic Studies: Addressing Cases without Matched Normal Samples

Arshi Arora, Li Luo, Caroline E. Kostrzewa, Allison Reiner, Venkatraman Seshan, Marc S. Ernstoff, Sharon N. Edmiston, Kathleen Conway, Ivan Gorlov, Klaus Johannes BUSAM, Irene Orlow, Eva Hernando, Nancy E. Thomas, Marianne Berwick, Colin B. Begg, , InterMEL

The paper improves somatic mutation detection in tumor samples without matched normal samples by estimating tumor purity and variant allele frequency, reducing false positive and negative rates.

bioRxiv · Oct 28, 2024 · PreprintGenomics

Pan-cancer identification of clinically relevant genomic subtypes using outcome-weighted integrative clustering

Arshi Arora, Adam B. Olshen, Venkatraman Seshan, Ronglai Shen

This paper introduces survClust, an outcome-weighted clustering algorithm that identifies clinically relevant genomic subtypes in pan-cancer data, outperforming unsupervised methods in detecting subpopulations with aggressive clinical behavior.

Genome Medicine · Dec 1, 2020Peer-reviewedGenomics