Optimal reconstruction of graph evolution dynamics for duplication-based models

Name: Optimal reconstruction of graph evolution dynamics for duplication-based models
Author: Gilmour, S., Sefer, Emre

İsim	Optimal reconstruction of graph evolution dynamics for duplication-based models
Yazar	Gilmour, S., Sefer, Emre
Basım Tarihi:	2024-02-29
Basım Yeri	- Springer Nature
Konu	PPI, Ancestral history reconstruction, Duplication-mutation with random mutation, DMR
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1860-949X
Kayıt Numarası	e5a6dd92-74ba-49c8-9de5-9c9f2425f06d
Lokasyon	Computer Science
Tarih	2024-02-29
Örnek Metin	The research on biological network evolution and graph growth models, such as the Duplication-Mutation with Random Mutation (DMR) model, enable us to characterize the protein interaction network's evolutionary dynamics founded on duplication and divergence via mutation in a principled way. The existing approaches to reconstruct historical ancestral graphs for DMR model mainly focus on greedy approaches and results in suboptimal solutions. In this study, we come up with ILP-DMR, a novel Integer Linear Programming (ILP)-based formulation, to reconstruct historical PPI graphs by likelihood maximization over DMR model. We assess the effectiveness of our approach in reconstructing the history of synthetic as well as optimal history of the proteins from the families of bZIP transcription factors. In comparison to the existing techniques, solutions returned by our ILP-DMR have a higher likelihood and are more robust to model mismatch and noise in the data. Solutions extracted by ILP-DMR have a higher likelihood than the existing methods, and our solutions better agree with the biological findings of different studies. Our datasets and code are available at https://github.com/seferlab/dmrhistory.
DOI	10.1007/978-3-031-53499-7_38
Cilt	1142

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Optimal reconstruction of graph evolution dynamics for duplication-based models

Yazar Gilmour, S., Sefer, Emre

Basım Tarihi 2024-02-29

Basım Yeri - Springer Nature

Konu PPI, Ancestral history reconstruction, Duplication-mutation with random mutation, DMR

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1860-949X

Kayıt Numarası e5a6dd92-74ba-49c8-9de5-9c9f2425f06d

Lokasyon Computer Science

Tarih 2024-02-29

Örnek Metin The research on biological network evolution and graph growth models, such as the Duplication-Mutation with Random Mutation (DMR) model, enable us to characterize the protein interaction network's evolutionary dynamics founded on duplication and divergence via mutation in a principled way. The existing approaches to reconstruct historical ancestral graphs for DMR model mainly focus on greedy approaches and results in suboptimal solutions. In this study, we come up with ILP-DMR, a novel Integer Linear Programming (ILP)-based formulation, to reconstruct historical PPI graphs by likelihood maximization over DMR model. We assess the effectiveness of our approach in reconstructing the history of synthetic as well as optimal history of the proteins from the families of bZIP transcription factors. In comparison to the existing techniques, solutions returned by our ILP-DMR have a higher likelihood and are more robust to model mismatch and noise in the data. Solutions extracted by ILP-DMR have a higher likelihood than the existing methods, and our solutions better agree with the biological findings of different studies. Our datasets and code are available at https://github.com/seferlab/dmrhistory.

DOI 10.1007/978-3-031-53499-7_38

Cilt 1142