Author

A. Sedigh

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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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Approach:statistical model· 1

Frequent Co-Authors

R. E. Tomlinson· 2 shared A. Tamayol· 1 shared B. Rasulev· 1 shared G. Casanola-Martin· 1 shared H. Pham-The· 1 shared J. E. Tulipan· 1 shared J. Quint· 1 shared K. Iduoku· 1 shared

Activity by Year

2020-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersBioengineering

R. E. Tomlinson

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Author profile →

3 papers

Recent·Most cited

Design and Evaluation of Nanoscale Materials with Programmed Responsivity towards Epigenetic Enzymes

P. Ray, A. Sedigh, M. Confeld, L. Alhalhooly, K. Iduoku, G. Casanola-Martin, H. Pham-The, B. Rasulev, Y. Choi, Z. Yang, S. Mallik, M. Quadir

This paper designs and evaluates nanomaterials that disassemble in response to HDAC enzymes, revealing a mechanism based on deacetylation-induced amphiphilicity reduction.

bioRxiv · Mar 26, 2024Bioengineering

Approximating Scaffold Printability Utilizing Computational Methods

A. Sedigh, P. Ghelich, J. Quint, M. Samandari, A. Tamayol, R. E. Tomlinson

The paper uses an adaptive neuro-fuzzy inference system to optimize printing parameters for gelatin-based bioinks, improving the precision and robustness of extrusion 3D bioprinting.

bioRxiv · Jul 11, 2022Bioengineering

Utilizing Q-Learning to Generate 3D Vascular Networks for Bioprinting Bone

A. Sedigh, J. E. Tulipan, M. R. Rivlin, R. E. Tomlinson

This study uses Q-learning to generate 3D vascular networks for bioprinting bone grafts, producing low-tortuosity vessels suitable for patient-specific geometries.

bioRxiv · Oct 8, 2020 · PreprintBioengineering