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PreprintCC BYbioRxiv

Cophylogeny simulators are not interchangeable: similarities, differences and structural biases in synthetic host-symbiont

  • G. Di Palma
  • C. Matias
  • B. Sinaimeri1
  1. 1Luiss University
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Abstract

Synthetic data are becoming increasingly important for computational studies of cophylogeny, including machine learning inference, benchmarking, and method testing. Several generators have been proposed to produce such data, but each relies on different assumptions about host-symbiont coevolution. These assumptions are often implicit and rarely examined, even though results can depend strongly on the synthetic model being used. In this article, we present a systematic structural analysis of representative cophylogeny generators under controlled scenarios. The goal is to make their assumptions explicit and to understand how these choices shape the synthetic data they produce as well as the conclusions that may be drawn from them.

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Cite

BibTeXRIS
BibTeX
@misc{palma2026cophylogeny,
  title = {{Cophylogeny simulators are not interchangeable: similarities, differences and structural biases in synthetic host-symbiont}},
  author = {G. Di Palma and C. Matias and B. Sinaimeri},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.13.751162},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.13.751162v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Cophylogeny simulators are not interchangeable: similarities, differences and structural biases in synthetic host-symbiont
AU  - G. Di Palma
AU  - C. Matias
AU  - B. Sinaimeri
PY  - 2026
DA  - 2026/09/13
PB  - bioRxiv
DO  - 10.64898/2026.09.13.751162
UR  - https://www.biorxiv.org/content/10.64898/2026.09.13.751162v1
AB  - Synthetic data are becoming increasingly important for computational studies of cophylogeny, including machine learning inference, benchmarking, and method testing. Several generators have been proposed to produce such data, but each relies on different assumptions about host-symbiont coevolution. These assumptions are often implicit and rarely examined, even though results can depend strongly on the synthetic model being used. In this article, we present a systematic structural analysis of representative cophylogeny generators under controlled scenarios. The goal is to make their assumptions explicit and to understand how these choices shape the synthetic data they produce as well as the conclusions that may be drawn from them.
ER  -