Author

M. Alimohammadi

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· 2 Immunology· 1

Signature Assays, Organisms & Methods

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Research Focus:clustering· 2 Approach:deep learning· 2 Assay:imaging· 2 Organism:mouse· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:dimensionality reduction· 1 Approach:experimental assay· 1

Frequent Co-Authors

A. Ahmadian· 2 shared P. Farnia· 2 shared A. Amiri Tehrani Zade· 1 shared A. Mofayezi· 1 shared A. Rahmati· 1 shared B. Makki Abadi· 1 shared B. Makkiiabadi· 1 shared D. Sattari Khavas· 1 shared

Activity by Year

2021-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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2 shared papersBioengineering

A. Ahmadian

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

ImmunologybioRxivMar 5, 2022

mRNA-based vaccine candidate COReNAPCIN(R) induces robust humoral and cellular immunity in mice and non-human primates

R. Alimohammadi, M. Porgoo, M. Eftekhary…·M. Azizi

This paper reports that the mRNA-based vaccine COReNAPCIN(R) induces strong humoral and cellular immunity and protects against SARS-CoV-2 infection in mice and non-human primates.

DOI 10.1038/s41541-022-00528-3

mousenon-human primate

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BioengineeringbioRxivJul 28, 2021

Photoacoustic-MR Images Registration Based on Co-Sparse Analysis Model to Compensate for Brain Shift

P. Farnia, B. Makkiiabadi, M. Alimohammadi…·A. Ahmadian

This study proposes a co-sparse analysis model for registering intra-operative photoacoustic images with pre-operative MR images to improve brain shift compensation in neurosurgery.

DOI 10.3390/s22062399

mousespatial transcriptomics

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BioengineeringbioRxivPreprintJul 3, 2021

Accurate Automatic Glioma Segmentation in Brain MRI images Based on CapsNet

M. Jalili Aziz, A. Amiri Tehrani Zade, P. Farnia…·J. Alirezaie

This paper introduces SegCaps, a capsule network for glioma segmentation in MRI images, achieving higher accuracy with fewer data and parameters than U-Net.

DOI 10.1101/2021.07.03.450931

humanimaging

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