1.

Gene network inference by fusing data from diverse distributions

We present FUSENET, a Markov network formulation that infers networks from a collection of nonidentically distributed datasets. Our approach is computationally efficient and general: given any number of distributions from an exponential family, FUSENET represents model parameters through shared latent factors that define neighborhoods of network nodes.

COBISS.SI-ID: 1536334275

2.

Small network completion using frequent subnetworks

Prediction of missing or potential links and edges is currently the central theme in network analysis. We define a problem of small network completion, which deals with sets of small networks, possibly with no recorded temporal dynamics. This problem requires a different set of methods and evaluation procedures. We present a method named Hyspan that extracts frequent patterns from small networks and uses them to predict missing vertices and edges in new networks.

COBISS.SI-ID: 1536144835

3.

Data fusion by matrix factorization

In the paper we describe a data fusion approach with penalized matrix tri-factorization (DFMF) that simultaneously factorizes data matrices to reveal hidden associations. The approach can directly consider any data that can be expressed in a matrix, including those from feature-based representations, ontologies, associations and networks.

COBISS.SI-ID: 1536103875