Gene Self-Expressive Networks as a Generalization-Aware Tool to Model Gene Regulatory Networks.

ABSTRACT: Self-expressiveness is a mathematical property that aims at characterizing the relationship between instances in a dataset. This property has been applied widely and successfully in computer-vision tasks, time-series analysis, and to infer underlying network structures in domains including protein signaling interactions and social-networks activity. Nevertheless, despite its potential, self-expressiveness has not been explicitly used to infer gene networks. In this article, we present Generalizable Gene Self-Expressive Networks, a new, interpretable, and generalization-aware formalism to model gene networks, and we propose two methods: GXN•EN and GXN•OMP, based respectively on ElasticNet and OMP (Orthogonal Matching Pursuit), to infer and assess Generalizable Gene Self-Expressive Networks.

SUBMITTER: Peignier S 

PROVIDER: S-EPMC10046116 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature