In this work, we study the problem of within-network relational learning and inference, where models are learned on a partially labeled relational dataset and then are applied to ...
Background: Development of high-throughput methods for measuring DNA interactions of transcription factors together with computational advances in short motif inference algorithms...
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This p...
Data is often encumbered by restrictions on the ways in which it may be used. These restrictions on usage may be determined by statute, by contract, by custom, or by common decenc...
Chris Hanson, Tim Berners-Lee, Lalana Kagal, Geral...
Undiscovered relationships in a data set may confound analyses, particularly those that assume data independence. Such problems occur when characters used for phylogenetic analyse...
Anne M. Maglia, Jennifer L. Leopold, Venkat Ram Gh...