Probabilistic Inference of Biological Networks via Data Integration
Joint Authors
Rogers, Mark F.
Campbell, Colin
Ying, Yiming
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-22
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
There is significant interest in inferring the structure of subcellular networks of interaction.
Here we consider supervised interactive network inference in which a reference set of known network links and nonlinks is used to train a classifier for predicting new links.
Many types of data are relevant to inferring functional links between genes, motivating the use of data integration.
We use pairwise kernels to predict novel links, along with multiple kernel learning to integrate distinct sources of data into a decision function.
We evaluate various pairwise kernels to establish which are most informative and compare individual kernel accuracies with accuracies for weighted combinations.
By associating a probability measure with classifier predictions, we enable cautious classification, which can increase accuracy by restricting predictions to high-confidence instances, and data cleaning that can mitigate the influence of mislabeled training instances.
Although one pairwise kernel (the tensor product pairwise kernel) appears to work best, different kernels may contribute complimentary information about interactions: experiments in S.
cerevisiae (yeast) reveal that a weighted combination of pairwise kernels applied to different types of data yields the highest predictive accuracy.
Combined with cautious classification and data cleaning, we can achieve predictive accuracies of up to 99.6%.
American Psychological Association (APA)
Rogers, Mark F.& Campbell, Colin& Ying, Yiming. 2015. Probabilistic Inference of Biological Networks via Data Integration. BioMed Research International،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1056447
Modern Language Association (MLA)
Rogers, Mark F.…[et al.]. Probabilistic Inference of Biological Networks via Data Integration. BioMed Research International No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1056447
American Medical Association (AMA)
Rogers, Mark F.& Campbell, Colin& Ying, Yiming. Probabilistic Inference of Biological Networks via Data Integration. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1056447
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1056447