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 Jun
- Container
- APL quantum
- Publisher
- Not recorded
- Open access
- yes
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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 = {Alter O and Newman E and Ponnapalli SP and Tsai JW},
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 - Alter O AU - Newman E AU - Ponnapalli SP AU - Tsai JW PY - 2026 JO - APL quantum DO - 10.1063/5.0305656 UR - https://doi.org/10.1063/5.0305656 ER -
APA
O, A., E, N., SP, P., & JW, T. (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
- pubmed · retrieved 2026-09-25T04:07:57.388Z