Parameterized algorithms for identifying gene co-expression modules via weighted clique decomposition.
Proc 2021 SIAM Conf Appl Comput Discret Algorithms (2021)
; 2021: 111-122, 2021.
Article
en En
| MEDLINE
| ID: mdl-35391741
ABSTRACT
We present a new combinatorial model for identifying regulatory modules in gene co-expression data using a decomposition into weighted cliques. To capture complex interaction effects, we generalize the previously-studied weighted edge clique partition problem. As a first step, we restrict ourselves to the noise-free setting, and show that the problem is fixed parameter tractable when parameterized by the number of modules (cliques). We present two new algorithms for finding these decompositions, using linear programming and integer partitioning to determine the clique weights. Further, we implement these algorithms in Python and test them on a biologically-inspired synthetic corpus generated using real-world data from transcription factors and a latent variable analysis of co-expression in varying cell types.
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1
Banco de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Proc 2021 SIAM Conf Appl Comput Discret Algorithms (2021)
Año:
2021
Tipo del documento:
Article