Author

Alireza Kashani

Shahid Beheshti University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 2 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Research Focus:spatial domain detection· 1

Frequent Co-Authors

Maryam Malekzadeh· 2 shared Atanas Kamburov· 1 shared Eejung Kim· 1 shared Elizabeth Worstell· 1 shared Fatemeh HashemiiD· 1 shared Gad Getz· 1 shared Hadis Momtaz· 1 shared Heiko HorniD· 1 shared

Activity by Year

2015-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersNeuroscience

Maryam Malekzadeh

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3 papers

Recent·Most cited

Structural Directional Brain Behavior Asymmetry Across Cortical and Non-Cortical Regions

Fatemeh Hashemi, Hadis Momtaz, Maryam Malekzadeh, Alireza Kashani

The paper explores structural directional brain asymmetry in cortical and non-cortical regions and its relationship to behavioral measures, finding consistent asymmetry across multiple brain areas.

bioRxiv · Jun 3, 2026 · PreprintNeuroscience

Asymmetry Map of Human Brain in Young Adults

Maryam Malekzadeh, Alireza Kashani

The paper analyzes structural brain asymmetry in young adults using the Human Connectome Project dataset, finding significant rightward asymmetry in multiple brain regions and a strong correlation between cortical surface area and gray matter volume asymmetry.

bioRxiv · Dec 3, 2020 · PreprintNeuroscience

A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes

Heiko Horn, Michael S. Lawrence, Jessica Xin Hu, Elizabeth Worstell, Nina Ilic, Yashaswi Shrestha, Eejung Kim, Atanas Kamburov, Alireza Kashani, William C. Hahn, Jesse S. Boehm, Gad Getz, Kasper Lage

The paper quantifies network mutation burdens across 21 tumor types to identify putative cancer genes, finding 62 new candidates with greater sensitivity for low-frequency mutations.

bioRxiv · Aug 25, 2015 · PreprintCancer Biology