Adaptive Machine Learning Framework for Optimizing the Affinity Purification of Adeno‐Associated Viral Vectors
- DOI
- 10.1002/bit.70159
- Published
- 2026-01-19
- Container
- Biotechnology and Bioengineering
- Publisher
- Wiley
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1002/bit.70159,
title = {Adaptive Machine Learning Framework for Optimizing the Affinity Purification of Adeno‐Associated Viral Vectors},
author = {Kelvin P. Idanwekhai and Shriarjun Shastry and Morgan R. Hurst and Arianna Minzoni and Eduardo Barbieri and Luke Remmler and Eugene N. Muratov and Michael A. Daniele and Stefano Menegatti and Alexander Tropsha},
year = {2026},
journal = {Biotechnology and Bioengineering},
doi = {10.1002/bit.70159},
url = {https://doi.org/10.1002/bit.70159}
}RIS
TY - JOUR TI - Adaptive Machine Learning Framework for Optimizing the Affinity Purification of Adeno‐Associated Viral Vectors AU - Kelvin P. Idanwekhai AU - Shriarjun Shastry AU - Morgan R. Hurst AU - Arianna Minzoni AU - Eduardo Barbieri AU - Luke Remmler AU - Eugene N. Muratov AU - Michael A. Daniele AU - Stefano Menegatti AU - Alexander Tropsha PY - 2026 JO - Biotechnology and Bioengineering DO - 10.1002/bit.70159 UR - https://doi.org/10.1002/bit.70159 ER -
APA
Idanwekhai, K. P., Shastry, S., Hurst, M. R., Minzoni, A., Barbieri, E., Remmler, L., Muratov, E. N., Daniele, M. A., Menegatti, S., & Tropsha, A. (2026). Adaptive Machine Learning Framework for Optimizing the Affinity Purification of Adeno‐Associated Viral Vectors. Biotechnology and Bioengineering. https://doi.org/10.1002/bit.70159
Source records
- crossref · retrieved 2026-09-25T07:29:58.076Z