Roni Rosenfeld

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BACKGROUND Mathematical and computational models provide valuable tools that help public health planners to evaluate competing health interventions, especially for novel circumstances that cannot be examined through observational or controlled studies, such as pandemic influenza. The spread of diseases like influenza depends on the mixing patterns within(More)
The Gene Ontology (GO) is extensively used to analyze all types of high-throughput experiments. However, researchers still face several challenges when using GO and other functional annotation databases. One problem is the large number of multiple hypotheses that are being tested for each study. In addition, categories often overlap with both direct(More)
James Allan (editor), Jay Aslam, Nicholas Belkin, Chris Buckley, Jamie Callan, Bruce Croft (editor), Sue Dumais, Norbert Fuhr, Donna Harman, David J. Harper, Djoerd Hiemstra, Thomas Hofmann, Eduard Hovy, Wessel Kraaij, John Lafferty, Victor Lavrenko, David Lewis, Liz Liddy, R. Manmatha, Andrew McCallum, Jay Ponte, John Prager, Dragomir Radev, Philip Resnik,(More)
We introduce a new kind of language model, which models whole sentences or utterances directly using the Maximum Entropy paradigm. The new model is conceptually simpler, and more naturally suited to modeling whole-sentence phenomena, than the conditional ME models proposed to date. By avoiding the chain rule, the model treats each sentence or utterance as a(More)
Global transcript levels throughout the cell cycle have been characterized using microarrays in several species. Early analysis of these experiments focused on individual species. More recently, a number of studies have concluded that a surprisingly small number of genes conserved in two or more species are periodically transcribed in these species.(More)
Influenza A virus is characterized by high genetic diversity. However, most of what is known about influenza evolution has come from consensus sequences sampled at the epidemiological scale that only represent the dominant virus lineage within each infected host. Less is known about the extent of within-host virus diversity and what proportion of this(More)
Learning the structures of large undirected graphical models from data is an active research area and has many potential applications in various domains, including molecular biology, social science, marketing data analysis, among others. The estimated structures provide semantic clarity, the possibility of causal interpretation, and ease of integration with(More)
BACKGROUND Agent based models (ABM) are useful to explore population-level scenarios of disease spread and containment, but typically characterize infected individuals using simplified models of infection and symptoms dynamics. Adding more realistic models of individual infections and symptoms may help to create more realistic population level epidemic(More)
BACKGROUND In December 2009, when the H1N1 influenza pandemic appeared to be subsiding, public health officials and unvaccinated individuals faced the question of whether continued H1N1 immunization was still worthwhile. PURPOSE To delineate what combinations of possible mechanisms could generate a third pandemic wave and then explore whether vaccinating(More)
We describe two attempt to improve our stochastic language models. In the first, we identify a systematic overestimation in the traditional backoff model, and use statisticalreasoning to correct it. Our modification results in up to 6% reduction in the perplexity of various tasks. Although the improvement is modest, it is achieved with hardly any increasein(More)