Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are...
Sriraam Natarajan, Tushar Khot, Kristian Kersting,...
This paper addresses the protein classification problem, and explores how its accuracy can be improved by using information from time-course gene expression data. The methods are ...
Antonina Mitrofanova, Samantha Kleinberg, Jane Car...
Background: Most existing algorithms for the inference of the structure of gene regulatory networks from gene expression data assume that the activity levels of transcription fact...
Background: One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a...
T. Ian Simpson, J. Douglas Armstrong, Andrew P. Ja...
Automatic recognition of human affective states is still a largely unexplored and challenging topic. Even more issues arise when dealing with variable quality of the inputs or aim...