Maximum likelihood reconstruction of ancestral networks by integer linear programming | Zendy

Vaibhav Rajan | Zendy; Ziqi Zhang | Zendy; Carl Kingsford | Zendy; Xiuwei Zhang | Zendy

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Maximum likelihood reconstruction of ancestral networks by integer linear programming

Author(s) -

Vaibhav Rajan,

Ziqi Zhang,

Carl Kingsford,

Xiuwei Zhang

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa931

Subject(s) - heuristics , computer science , integer programming , correctness , python (programming language) , linear programming , biological network , theoretical computer science , algorithm , mathematical optimization , mathematics , computational biology , biology , operating system

The study of the evolutionary history of biological networks enables deep functional understanding of various bio-molecular processes. Network growth models, such as the Duplication-Mutation with Complementarity (DMC) model, provide a principled approach to characterizing the evolution of protein-protein interactions (PPIs) based on duplication and divergence. Current methods for model-based ancestral network reconstruction primarily use greedy heuristics and yield sub-optimal solutions.

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