.png){kind=icon}
Return
Protein Function Prediction with Incomplete Annotations
.png){kind=icon}
OA
AI
DOI：10.1109/TCBB.2013.142.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Protein function prediction
multi-label learning
incomplete annotations
Journal
I
IF:
3.4
Papers:
3.3K
Citations:
6.4K
Organization
View more organizations
Cited Papers
ShareSaveShareSaveThe FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes.png)
NUCLEIC ACIDS RESEARCH
OAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAIShareSaveShareSaveShareSaveShareSaveShareSave View more