End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins.
- DOI
- 10.1021/acsbiomaterials.1c01343
- Published
- 2022 Mar 14
- Container
- ACS biomaterials science & engineering
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1021/acsbiomaterials.1c01343,
title = {End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins.},
author = {Yu CH and Chen W and Chiang YH and Guo K and Martin Moldes Z and Kaplan DL and Buehler MJ},
year = {2022},
journal = {ACS biomaterials science \& engineering},
doi = {10.1021/acsbiomaterials.1c01343},
url = {https://doi.org/10.1021/acsbiomaterials.1c01343}
}RIS
TY - JOUR TI - End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins. AU - Yu CH AU - Chen W AU - Chiang YH AU - Guo K AU - Martin Moldes Z AU - Kaplan DL AU - Buehler MJ PY - 2022 JO - ACS biomaterials science & engineering DO - 10.1021/acsbiomaterials.1c01343 UR - https://doi.org/10.1021/acsbiomaterials.1c01343 ER -
APA
CH, Y., W, C., YH, C., K, G., Z, M. M., DL, K., & MJ, B. (2022). End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins.. ACS biomaterials science & engineering. https://doi.org/10.1021/acsbiomaterials.1c01343
Source records
- pubmed · retrieved 2026-09-26T03:28:25.365Z
- europe-pmc · retrieved 2026-09-26T03:28:25.370Z