2022/02/10 by Miguel Romero, Jorge Finke, Camilo Rocha · 16 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial intelligence #Artificial neural network #Bioinformatics and Genomic Networks #Classifier (UML) #Computer science #Data mining #Gene expression and cancer classification #Machine Learning in Bioinformatics #Machine learning #Multi-label classification #One-class classification #Probabilistic logic #Random forest #Subnetwork #Supervised learning #cs.LG
paper · pdf · open access · doi:10.1007/s41109-022-00445-3
published in Applied Network Science 7(1) (Springer Nature)
openalex publication_date 2022/02/10 · arxiv created 2022/03/23 · arxiv updated 2022/03/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. This paper presents a general prediction model to hierarchical multi-label classification (HMC), where the attributes to be inferred can be specified as a strict poset. It is based on a top-down classification approach that addresses hierarchical multi-label classification with supervised learning by building a local classifier per class. The proposed model is showcased with a case study on the prediction of gene functions for Oryza sativa Japonica, a variety of rice. It is compared to the Hierarchical Binomial-Neighborhood, a probabilistic model, by evaluating both approaches in terms of prediction performance and computational cost. The results in this work support the working hypothesis that the proposed model can achieve good levels of prediction efficiency, while scaling up in relation to the state of the art.