Using the Gene Ontology Hierarchy when Predicting Gene Function
By Sara Mostafavi, Quaid D. Morris. 2009
33 citations recorded by the source corpus.
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.
Subfields: Molecular Biology, Evaluation & Benchmarks, Computational Biology & Bioinformatics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Marinka Zitnik
- Jure Leskovec
- Asa Ben-Hur
- Petar Velickovic
- William L. Hamilton
- Rex Ying
- Stephan Günnemann
- Xinchao Wang
In Evaluation & Benchmarks
In Computational Biology & Bioinformatics
- Jure Leskovec
- Marinka Zitnik
- Asa Ben-Hur
- Petar Velickovic
- Yiding Yang
- Xinchao Wang
- Bernard Ghanem
- Guohao Li
Citations in the corpus
- Predicting multicellular function through multi-layer tissue networks (2017, methodology): “(1) First, current methods for cellular function prediction on networks (Mostafavi and Morris, 2009; Radivojac et al., 2013; Vidulin et al., 2016; Zitnik and Zupan, 2015) do not model networks with regards to patterns that span tissues, organs, and cellular systems.”
- Multi-view prediction of protein function (2011, methodology): “Mostafavi and Morris proposed extensions of their GeneMANIA method that directly predict a hierarchical labeling of a protein [18].”