Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information.

<p>(A), (B) Algorithm performance, evaluated over 50 simulated datasets generated as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.g001" target="_blank">Fig 1</a> with <i>N</i> = 3 true g...

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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 ).
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Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information.

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