Using synthetic data to test group-searching algorithms in a context where the correct grouping of species is known and uniquely defined.

(A) We adopt a resource competition model with cross-feeding. The reaction network is assumed to form a linear degradation chain 1 → 2 → ⋯ → N with the end-product concentration (metabolite N, orange) taken as the function of interest (shown with <i&...

Full description

Saved in:

Bibliographic Details
Main Author:	Yuanchen Zhao (12905580) (author)
Other Authors:	Otto X. Cordero (7308872) (author), Mikhail Tikhonov (3259908) (author)
Published:	2024
Subjects:	Evolutionary Biology Ecology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified two proposed previously resource competition framework ensemble quotient optimization detailed microscopic descriptions de novo </ identify useful groupings modeling community function div >< p compare three regression coarse representations predictive use synthetic data group methods offer predictive coarsening coarser representations several approaches recent work prohibitively complex multiple groups microbial ecology manifestly nonlinear key goal groupings even former could empirical success driven way based methods based algorithms approach ).
Tags:	Add Tag No Tags, Be the first to tag this record!

Using synthetic data to test group-searching algorithms in a context where the correct grouping of species is known and uniquely defined.

Similar Items