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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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Yuanchen Zhao (12905580) (author)
مؤلفون آخرون:	Otto X. Cordero (7308872) (author), Mikhail Tikhonov (3259908) (author)
منشور في:	2024
الموضوعات:	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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