Quantum mechanics-based multitensor AI/ML uniquely able to discover, validate, and interpret predictors from small-cohort noisy high-dimensional multiomic data
- DOI
- 10.1063/5.0305656
- Published
- 2026-06-01
- Container
- APL Quantum
- Publisher
- AIP Publishing
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1063/5.0305656,
title = {Quantum mechanics-based multitensor AI/ML uniquely able to discover, validate, and interpret predictors from small-cohort noisy high-dimensional multiomic data},
author = {Orly Alter and Elizabeth Newman and Sri Priya Ponnapalli and Jessica W. Tsai},
year = {2026},
journal = {APL Quantum},
doi = {10.1063/5.0305656},
url = {https://doi.org/10.1063/5.0305656}
}RIS
TY - JOUR TI - Quantum mechanics-based multitensor AI/ML uniquely able to discover, validate, and interpret predictors from small-cohort noisy high-dimensional multiomic data AU - Orly Alter AU - Elizabeth Newman AU - Sri Priya Ponnapalli AU - Jessica W. Tsai PY - 2026 JO - APL Quantum DO - 10.1063/5.0305656 UR - https://doi.org/10.1063/5.0305656 ER -
APA
Alter, O., Newman, E., Ponnapalli, S. P., & Tsai, J. W. (2026). Quantum mechanics-based multitensor AI/ML uniquely able to discover, validate, and interpret predictors from small-cohort noisy high-dimensional multiomic data. APL Quantum. https://doi.org/10.1063/5.0305656
Source records
- crossref · retrieved 2026-11-04T10:10:10.516Z