title: Protein Function Prediction with Incomplete Annotations.
abstract: Automated protein function prediction is one of the grand challenges in computational biology. Multi-label learning is widely used to predict functions of proteins. Most of multi-label learning methods make prediction for unlabeled proteins under the assumption that the labeled proteins are completely annotated, i.e., without any missing functions. However, in practice, we may have a subset of the ground-truth functions for a protein, and whether the protein has other functions is unknown. To predict protein functions with incomplete annotations, we propose a Protein Function Prediction method with Weak-label Learning (ProWL) and its variant ProWL-IF. Both ProWL and ProWL-IF can replenish the missing functions of proteins. In addition, ProWL-IF makes use of the knowledge that a protein cannot have certain functions, which can further boost the performance of protein function prediction. Our experimental results on protein-protein interaction networks and gene expression benchmarks validate the effectiveness of both ProWL and ProWL-IF.
authors.forename: Guoxian
authors.initials: G
authors.lastname: Yu
authors.forename: Huzefa
authors.initials: H
authors.lastname: Rangwala
authors.forename: Carlotta
authors.initials: C
authors.lastname: Domeniconi
authors.forename: Guoji
authors.initials: G
authors.lastname: Zhang
authors.forename: Zhiwen
authors.initials: Z
authors.lastname: Yu
mesh_terms: D019295:Computational Biology / Q000379:methods*; D015233:Models, Statistical; D058977:Molecular Sequence Annotation / Q000379:methods; D060066:Protein Interaction Maps / Q000235:genetics; D011506:Proteins / Q000145:classification* / Q000235:genetics / Q000378:metabolism*; D059467:Transcriptome / Q000235:genetics
chemical_list: D011506:Proteins