Ensemble Learning Based Gene Regulatory Network Inference - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue International Journal on Artificial Intelligence Tools Année : 2023

Ensemble Learning Based Gene Regulatory Network Inference

Résumé

In the machine learning field, the technique known as ensemble learning aims at combining different base learners in order to increase the quality and the robustness of the predictions. Indeed, this approach has widely been applied to tackle, with success, real world problems from different domains, including computational biology. Nevertheless, despite their potential, ensembles combining results from different base learners have been understudied in the context of gene regulatory network inference. In this paper we applied genetic algorithms and frequent itemset mining, to design small but effective ensembles of gene regulatory network inference methods. These ensembles were evaluated and compared to well-established single and ensemble methods, on both real and synthetic datasets. Results showed that small ensembles, consisting of few but diverse base learners, enhance the exploration of the solution space, and compensate base learners biases, outperforming state-of-the-art methods. Results advocate for the use of such methods as gene regulatory network inference tools.
Fichier principal
Vignette du fichier
_IJAIT_extension__ensemble_based_GRN_inference (2).pdf (979.91 Ko) Télécharger le fichier
Origine : Publication financée par une institution

Dates et versions

hal-04044951 , version 1 (24-03-2023)

Licence

Paternité

Identifiants

Citer

S. Peignier, Baptiste Sorin, Federica Calevro. Ensemble Learning Based Gene Regulatory Network Inference. International Journal on Artificial Intelligence Tools, 2023, 32 (5), pp.113-120. ⟨10.1142/S0218213023600059⟩. ⟨hal-04044951⟩
9 Consultations
14 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More