Author

Mohammad Tanhaemami

Broad InstituteORCID 0000-0002-6498-1181Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Approach:experimental assay· 2 Organism:human· 2 Assay:bulk RNA-seq· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Brian D. CromptoniD· 2 shared David Stephen ShulmaniD· 2 shared Kelly S. KlegaiD· 2 shared Advaita Chakraborty· 1 shared Alejandra E. Aguilar-QuinteroiD· 1 shared Amr Salem· 1 shared Anne-Florence BlandiniD· 1 shared Babetta L. MarroneiD· 1 shared

Activity by Year

2019-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersCancer Biology

Brian D. CromptonORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Cancer BiologybioRxivPreprintSep 18, 2026

Clustered structural variant hotspots enable oncogenic addiction and plasticity in osteosarcoma

Shreya Raman, Thorsten Kaltenbacher, David Merrell…·Riaz Gillani

The paper identifies clustered structural variant hotspots in osteosarcoma, linking them to oncogenic addiction and plasticity through transcriptional changes and clinical detectability.

DOI 10.64898/2026.09.14.751472

humanwhole-genome sequencing

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Cancer BiologyTranslational OncologyPeer-reviewedJun 18, 2026

A novel combination of CDK4/6 and PI3K inhibitors exhibits highly synergistic activity and translational potential in Ewing sarcoma.

Helen F. Gloege, Maria Ana Isabel C. De Los Santos, Advaita Chakraborty…·Brian D. Crompton

This paper identifies a synergistic combination of CDK4/6 and PI3K inhibitors that shows strong anti-Ewing sarcoma activity and improved survival in a xenograft model.

1 citation

humanbulk RNA-seq

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Systems BiologyPhysical BiologyPeer-reviewedJun 25, 2019

Using Flow Cytometry and Multistage Machine Learning to Discover Label-Free Signatures of Algal Lipid Accumulation

Mohammad Tanhaemami, Elaheh Alizadeh, Claire K. Sanders…·Brian E. Munsky

The paper develops a label-free method using flow cytometry and machine learning to predict algal lipid content without fluorescent dyes.

10 citations

machine learning (classical)

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