Author

Marilyn M. Li

Children's Hospital of PhiladelphiaORCID 0000-0002-4253-2369Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genetics· 1

Signature Assays, Organisms & Methods

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Assay:whole-exome sequencing· 2 Research Focus:differential expression· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Ahmad N. Abou TayouniD· 1 shared Aime T. FrancoiD· 1 shared Amber R. IsazaiD· 1 shared Andrew J. BaueriD· 1 shared Chao WuiD· 1 shared Deanne TayloriD· 1 shared Erin ReichenbergeriD· 1 shared Jessica Clague DeHartiD· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperBioinformatics

Ahmad N. Abou TayounORCID

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2 papers

Recent·Most cited

Fusion-oncogenes are associated with increased metastatic capacity and persistent disease in pediatric thyroid cancers

Aime T. Franco, Julio C. Ricarte-Filho, Amber R. Isaza, Zachary Jones, Neil K. Jain, Sogol Mostoufi‐Moab, Lea F. Surrey, Theodore Willis Laetsch, Marilyn M. Li, Jessica Clague DeHart, Erin Reichenberger, Deanne Taylor, Ken Kazahaya, N. Scott Adzick, Andrew J. Bauer

This study shows that genetic subtypes of pediatric thyroid cancer, particularly RET/NTRK fusions, are linked to worse clinical outcomes compared to RAS- or BRAF-mutant subtypes.

Journal of Clinical Oncology · Jan 11, 2022Peer-reviewedGenetics

Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing

Chao Wu, Xiaonan Zhao, Mark Welsh, Kellianne Costello, Kajia Cao, Ahmad N. Abou Tayoun, Marilyn M. Li, Mahdi Sarmady

The paper presents a machine learning classifier to identify true somatic variants from next-generation sequencing data, achieving high specificity and sensitivity with minimal uncertainty.

Clinical Chemistry · Oct 31, 2019Peer-reviewedBioinformatics