Author

Zhu Shen

Harvard UniversityORCID 0000-0003-4564-5438Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Assay:bulk RNA-seq· 3 Research Focus:differential expression· 3 Organism:human· 3 Research Focus:cell type annotation· 2 Research Focus:benchmarking· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Anandi KrishnaniD· 3 shared Jesse W. RowleyiD· 2 shared Vanita S. Natu· 2 shared Abhishek SawalkariD· 1 shared Anna FalangaiD· 1 shared Anne Fortune· 1 shared Barry KevaneiD· 1 shared Caitríona ScaifeiD· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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3 shared papersPathology

Anandi KrishnanORCID

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3 papers

Recent·Most cited

PathologybioRxivPreprintApr 1, 2026

Platelets Outperform Leukocytes in Transcriptomic Liquid Biopsy Profiling of Myeloproliferative Neoplasms

Zhu Shen, Abhishek Sawalkar, Jason J. Wu…·Anandi Krishnan

This study compares platelet and leukocyte transcriptomes in MPN patients, finding platelets better capture myelofibrosis signatures and provide more accurate diagnosis.

DOI 10.64898/2026.03.30.714941

humanbulk RNA-seq

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PathologyBlood AdvancesPeer-reviewedJun 11, 2024

Platelet proteo-transcriptomic profiling validates mediators of thrombosis and proteostasis in patients with myeloproliferative neoplasms

Sarah Kelliher, Sara Gamba, Luisa Weiß…·Anandi Krishnan

The paper profiles the platelet proteome in MPN patients to identify mediators of thrombosis and proteostasis, validating key transcriptome findings.

23 citationstop 3%

humanproteomics

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GenomicsBloodPeer-reviewedNov 5, 2021

Platelet transcriptome yields progressive markers in chronic myeloproliferative neoplasms and identifies putative targets of therapy

Zhu Shen, Wenfei Du, Cecelia Perkins…·Anandi Krishnan

The paper identifies platelet transcriptome markers for predicting disease progression in MPNs and develops a machine learning model to predict advanced MF with high accuracy.

1 citation

humanbulk RNA-seq

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