CISPA
Browse
2023.02.24.529835v2.full.pdf (1.68 MB)

Biologically informed NeuralODEs for genome-wide regulatory dynamics

Download (1.68 MB)
preprint
posted on 2024-03-25, 14:01 authored by Intekhab Hossain, Viola Fanfani, Jonas Fischer, John Quackenbush, Rebekka Burkholz
Modeling dynamics of gene regulatory networks using ordinary differential equations (ODEs) allow a deeper understanding of disease progression and response to therapy, thus aiding in intervention optimization. Although there exist methods to infer regulatory ODEs, these are generally limited to small networks, rely on dimensional reduction, or impose non-biological parametric restrictions — all impeding scalability and explainability. PHOENIX is a neural ODE framework incorporating prior domain knowledge as soft constraints to infer sparse, biologically interpretable dynamics. Extensive experiments - on simulated and real data - demonstrate PHOENIX’s unique ability to learn key regulatory dynamics while scaling to the whole genome.

History

Primary Research Area

  • Trustworthy Information Processing

Open Access Type

  • Green

BibTeX

@misc{Hossain:Fanfani:Fischer:Quackenbush:Burkholz:2024, title = "Biologically informed NeuralODEs for genome-wide regulatory dynamics", author = "Hossain, Intekhab" AND "Fanfani, Viola" AND "Fischer, Jonas" AND "Quackenbush, John" AND "Burkholz, Rebekka", year = 2024, month = 1, doi = "10.1101/2023.02.24.529835" }

Usage metrics

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC