Algebraic model selection and experimental design in biological data science

DOI10.1016/j.aam.2021.102282zbMath1477.13051arXiv2101.09384OpenAlexW3210721534WikidataQ112880049 ScholiaQ112880049MaRDI QIDQ2665755

Jingzhen Hu, Anyu Zhang, Qingzhong Liang, Elena S. Dimitrova, Brandilyn Stigler

Publication date: 19 November 2021

Published in: Advances in Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2101.09384

zbMATH Keywords

Gröbner basesmodel selectionfinite fieldsexperimental designstandard monomialsbiological data science

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Gröbner bases; other bases for ideals and modules (e.g., Janet and border bases) (13P10)

Uses Software

• Macaulay2
• Gfan
• Python
• SQLite
• Points
• gfanInterface

Cites Work

• Data identification for improving gene network inference using computational algebra
• Geometric characterization of data sets with unique reduced Gröbner bases
• A computational algebra approach to the reverse engineering of gene regulatory networks
• Small Gröbner fans of ideals of points
• Mathematics for Machine Learning
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