Format

Send to

Choose Destination
BMC Bioinformatics. 2010 Jan 18;11 Suppl 1:S57. doi: 10.1186/1471-2105-11-S1-S57.

Active learning for human protein-protein interaction prediction.

Author information

1
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. mop13+bmc@pitt.edu

Abstract

BACKGROUND:

Biological processes in cells are carried out by means of protein-protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is resource-intensive; only about 38,000 interactions, out of a few hundred thousand expected interactions, are known today. Active machine learning can guide the selection of pairs of proteins for future experimental characterization in order to accelerate accurate prediction of the human protein interactome.

RESULTS:

Random forest (RF) has previously been shown to be effective for predicting protein-protein interactions. Here, four different active learning algorithms have been devised for selection of protein pairs to be used to train the RF. With labels of as few as 500 protein-pairs selected using any of the four active learning methods described here, the classifier achieved a higher F-score (harmonic mean of Precision and Recall) than with 3000 randomly chosen protein-pairs. F-score of predicted interactions is shown to increase by about 15% with active learning in comparison to that with random selection of data.

CONCLUSION:

Active learning algorithms enable learning more accurate classifiers with much lesser labelled data and prove to be useful in applications where manual annotation of data is formidable. Active learning techniques demonstrated here can also be applied to other proteomics applications such as protein structure prediction and classification.

PMID:
20122232
PMCID:
PMC3009530
DOI:
10.1186/1471-2105-11-S1-S57
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center