2012/05/09 by Sara Mostafavi, Quaid Morris, Mostafavi, Sara +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bioinformatics and Genomic Networks #Computational Engineering #FOS: Computer and information sciences #Finance #Gene expression and cancer classification #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Bioinformatics #and Science (cs.CE) #cs.CE #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1205.2622
Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)
arxiv created 2012/05/09 · openalex publication_date 2012/05/09 · arxiv updated 2012/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The problem of multilabel classification when the labels are related through a hierarchical categorization scheme occurs in many application domains such as computational biology. For example, this problem arises naturally when trying to automatically assign gene function using a controlled vocabularies like Gene Ontology. However, most existing approaches for predicting gene functions solve independent classification problems to predict genes that are involved in a given function category, independently of the rest. Here, we propose two simple methods for incorporating information about the hierarchical nature of the categorization scheme. In the first method, we use information about a gene's previous annotation to set an initial prior on its label. In a second approach, we extend a graph-based semi-supervised learning algorithm for predicting gene function in a hierarchy. We show that we can efficiently solve this problem by solving a linear system of equations. We compare these approaches with a previous label reconciliation-based approach. Results show that using the hierarchy information directly, compared to using reconciliation methods, improves gene function prediction.