Author

M. Abedi

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

Biology Disciplines

Click to filter author papers

Bioengineering· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:drug response / drug discovery· 2 Approach:experimental assay· 2 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Organism:human· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

A. Bar-Zion· 1 shared A. Hand· 1 shared A. J. Borst· 1 shared A. Khmelinskaia· 1 shared A. Lee-Gosselin· 1 shared A. Nourmahnad· 1 shared A. Sarajovi· 1 shared C. Dobbins· 1 shared

Activity by Year

2019-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

1 shared paperBioengineering

A. Bar-Zion

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Computational design of non-porous, pH-responsive antibody nanoparticles

E. C. Yang, R. Divine, M. C. Miranda, A. J. Borst, W. Sheffler, J. Z. Zhang, J. Decarreau, A. Sarajovi, M. Abedi, N. Goldbach, M. Ahlrichs, C. Dobbins, A. Hand, S. Cheng, M. Lamb, , D. Baker

This paper designs pH-responsive antibody nanoparticles with three distinct protein components that disassemble at tunable pH levels, enabling targeted delivery of biologics.

bioRxiv · Apr 17, 2023Bioengineering

Using a Motion Sensor to Categorize Low Back Pain Patients: A Machine Learning Approach

M. Abdollahi, S. Ashouri, M. Abedi, N. Azadeh-Fard, M. Parnianpour, E. Rashedi

This paper develops a machine learning model using kinematic data from motion sensors to classify low back pain patients into high and low-medium risk groups, achieving ~75% accuracy with SVM.

bioRxiv · Oct 13, 2019Bioengineering

Acoustically Detonated Biomolecules for Genetically Encodable Inertial Cavitation

A. Bar-Zion, A. Nourmahnad, D. Mittelstein, S. Yoo, D. Malounda, M. Abedi, A. Lee-Gosselin, D. Maresca, M. G. Shapiro

This paper demonstrates the use of gas vesicles engineered to produce mechanical effects via ultrasound, enabling targeted cell lysis and therapeutic payload release in vitro, in cellulo, and in vivo.

bioRxiv · Apr 29, 2019 · PreprintBioengineering