Author

Catherine H. Feng

Dana-Farber Cancer InstituteORCID 0000-0002-7413-9748Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Assay:clinical cohort / trial· 1 Research Focus:clustering· 1

Frequent Co-Authors

Alexander GuseviD· 1 shared Alexander N. Shoushtari· 1 shared Alexander PaniD· 1 shared Chi‐Ping DayiD· 1 shared Daniel LeeiD· 1 shared David LiuiD· 1 shared Fei DengiD· 1 shared Frank Stephen HodiiD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperCancer Biology

Alexander GusevORCID

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2 papers

Recent·Most cited

Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients

Catherine H. Feng, Fei Deng, Mary Lenora Disis, Nan Gao, Lanjing Zhang

The paper evaluates ML model fairness in classifying survival outcomes for colorectal and lung cancer patients across sociodemographic groups using TCGA data, finding biases and proposing methods to reduce disparities.

Briefings in Bioinformatics · Jul 1, 2025Peer-reviewedBioinformatics

Clinico-genomic features predict distinct metastatic phenotypes in cutaneous melanoma

Tyler J. Aprati, Chi‐Ping Day, Daniel Lee, Alexander Pan, Justin Jee, Giuseppe Tarantino, Micheal P. Manos, Hannah Mae Faulkner, Marta M. Holovatska, Karam Khaddour, Catherine H. Feng, Kelly P. Burke, Marc Glettig, Zoë Weaver Ohler, Rajaâ El Meskini, , David Liu

This study identifies distinct metastatic phenotypes in cutaneous melanoma using clinico-genomic data and machine learning, linking specific mutations and clinical factors to organ-specific metastasis.

bioRxiv · Jan 24, 2025 · PreprintCancer Biology