Author

Mohammad Haghir Ebrahimabadi

National Institutes of HealthORCID 0000-0001-6265-4251Unclaimed 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:benchmarking· 1 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Approach:optimal transport· 1 Research Focus:phylogenetics / evolution· 1 Assay:scRNA-seq· 1 Approach:statistical model· 1

Frequent Co-Authors

Erfan Sadeqi AzeriD· 2 shared S. Cenk ŞahinalpiD· 2 shared Salem MalikićiD· 2 shared Farid Rashidi MehrabadiiD· 1 shared Roni Khardon· 1 shared

Activity by Year

2020-2021 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 9 mutual co-author links

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2 shared papersBioinformatics

Erfan Sadeqi AzerORCID

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2 papers

Recent·Most cited

BioinformaticsJournal of Computational BiologyPeer-reviewedJul 23, 2021

Studying the history of tumor evolution from single-cell sequencing data by exploring the space of binary matrices

Salem Malikić, Farid Rashidi Mehrabadi, Erfan Sadeqi Azer…·S. Cenk Şahinalp

This paper reviews methods for inferring tumor evolutionary trees using binary matrix optimization and discusses a deep learning approach for topological inference.

6 citations

bulk RNA-seqstatistical model

𝕏 PostBluesky

BioinformaticsiSciencePeer-reviewedOct 7, 2020

Tumor Phylogeny Topology Inference via Deep Learning

Erfan Sadeqi Azer, Mohammad Haghir Ebrahimabadi, Salem Malikić…·S. Cenk Şahinalp

This paper introduces deep learning methods to infer tumor phylogeny topology and feasibility from single-cell genotype data, demonstrating the potential of data-driven approaches in cancer evolution analysis.

21 citations

scRNA-seqdeep learning

𝕏 PostBluesky