Abstract

Abstract
Transcriptional regulation play an important role in every cellular decision. Gaining an understanding of the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We introduce logic-based regulation models based on state-of-the-art work on statistical relational learning, to show that network hypotheses can be generated from existing gene expression data for use by experimental biologists.

Abstract
The pharmaceutical industry, consumer protection groups, users of medications and government oversight agencies are all strongly interested in identifying adverse reactions to drugs. While a clinical trial of a drug may use only a thousand patients, once a drug is released on the market it may be taken by millions of patients. As a result, in many cases adverse drug events (ADEs) are observed in the broader population that were not identified during clinical trials. Therefore, there is a need for continued, post-marketing surveillance of drugs to identify previously-unanticipated ADEs. This paper casts this problem as a reverse machine learning task, related to relational subgroup discovery and provides an initial evaluation of this approach based on experiments with an actual EMR/EHR and known adverse drug events. Copyright

Abstract
Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connections between related medications or diseases. One way to capture the latent structure is via a relational clustering of objects. We propose a novel approach that, instead of pre-clustering the objects, performs a demand-driven clustering during learning. We evaluate our algorithm on three real-world tasks where the goal is to use EMRs to predict whether a patient will have an adverse reaction to a medication. We find that our approach is more accurate than performing no clustering, pre-clustering, and using expert-constructed medical heterarchies. Copyright 2012 by the author(s)/owner(s).

Abstract
We are proud to introduce this special issue of LIPIcs - Leibniz International Proceedings in Informatics, dedicated to the technical communications accepted for the 28th International Conference on Logic Programming (ICLP). © Agostino Dovier and Vítor Santos Costa.

Abstract
The Dataflow execution model has been shown to be a good way of exploiting TLP, making parallel programming easier. In this model, tasks must be mapped to processing elements (PEs) considering the trade-off between communication and parallelism. Previous work on scheduling dependency graphs have mostly focused on directed a cyclic graphs, which are not suitable for dataflow (loops in the code become cycles in the graph). Thus, we present the SCC-Map: a novel static mapping algorithm that considers the importance of cycles during the mapping process. To validate our approach, we ran a set of benchmarks in on our dataflow simulator varying the communication latency, the number of PEs in the system and the placement algorithm. Our results show that the benchmark programs run significantly faster when mapped with SCC-Map. Moreover, we observed that SCC-Map is more effective than the other mapping algorithms when communication latency is higher. © 2012 IEEE.