The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Kernel methods for predicting protein--protein interactions
Bioinformatics
Convex optimization techniques for fitting sparse Gaussian graphical models
ICML '06 Proceedings of the 23rd international conference on Machine learning
Exploring the functional landscape of gene expression
Bioinformatics
The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
The Journal of Machine Learning Research
Inferring gene interaction networks from ISH images via kernelized graphical models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
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Inference of gene interaction networks from expression data usually focuses on either supervised or unsupervised edge prediction from a single data source. However, in many real world applications, multiple data sources, such as microarray and ISH measurements of mRNA abundances, are available to offer multi-view information about the same set of genes. We propose NP-MuScL (nonparanormal multi-source learning) to estimate a gene interaction network that is consistent with such multiple data sources, which are expected to reflect the same underlying relationships between the genes. NP-MuScL casts the network estimation problem as estimating the structure of a sparse undirected graphical model. We use the semiparametric Gaussian copula to model the distribution of the different data sources, with the different copulas sharing the same precision (i.e., inverse covariance) matrix, and we present an efficient algorithm to estimate such a model in the high dimensional scenario. Results are reported on synthetic data, where NP-MuScL outperforms baseline algorithms significantly, even in the presence of noisy data sources. Experiments are also run on two real-world scenarios: two yeast microarray data sets, and three Drosophila embryonic gene expression data sets, where NP-MuScL predicts a higher number of known gene interactions than existing techniques.