Author

E. Mamourian

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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Bioengineering· 3

Signature Assays, Organisms & Methods

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

Frequent Co-Authors

A. Abdulkadir· 2 shared A. Singh· 2 shared C. Davatzikos· 2 shared D. Srinivasan· 2 shared G. Erus· 2 shared I. Nasrallah· 2 shared J. Wen· 2 shared S. M. Resnick· 2 shared

Activity by Year

2022-2022 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioengineering

A. Abdulkadir

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

The Radiogenomic and Spatiogenomic Landscapes of Glioblastoma, and Their Relationship to Oncogenic Drivers

A. Fathi Kazerooni, H. Akbari, X. Hu, V. Bommineni, D. Grigoriadis, E. Toorens, C. Sako, E. Mamourian, D. Ballinger, R. Sussman, A. Singh, I. I. Verginadis, N. Dahmane, C. Koumenis, Z. A. Binder, , C. Davatzikos

This study identifies imaging and spatial biomarkers in glioblastoma that correlate with molecular features and oncogenic drivers using machine learning on MRI data.

bioRxiv · Dec 15, 2022Bioengineering

Genetic, Clinical Underpinnings of Brain Change Along Two Neuroanatomical Dimensions of Clinically-defined Alzheimer's Disease

J. Wen, Z. Yang, I. Nasrallah, Y. Cui, G. Erus, D. Srinivasan, A. Abdulkadir, E. Mamourian, I. Skampardoni, G. Hwang, A. Singh, M. Bergman, J. Bao, E. Varol, Z. Zhou, , Sh

This study identifies two brain atrophy dimensions in Alzheimer's disease using Surreal-GAN and links them to genetic and clinical factors, revealing distinct pathological mechanisms.

bioRxiv · Sep 16, 2022 · PreprintBioengineering

Multiscale functional connectivity patterns of the aging brain learned from rsfMRI data of 4,259 individuals of the multi-cohort iSTAGING study

Z. Zhou, D. Srinivasan, H. Li, A. Abdulkadir, I. Nasrallah, J. Wen, J. Doshi, G. Erus, E. Mamourian, N. R. Bryan, D. A. Wolk, L. Beason-Held, S. M. Resnick, T. D. Satterthwaite, C. Davatzikos, , Y. Fan

The paper develops a brain age prediction model using multiscale functional connectivity from rsfMRI data of 4,259 individuals, showing that multiscale measures improve prediction and that brain age gaps correlate with cognitive and clinical measures.

bioRxiv · Jul 27, 2022 · PreprintBioengineering