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
Structural activity prediction is one of the most important tasks in chemoinformatics. The goal is to predict a property of interest given structural data on a set of small compounds or drugs. Ideally, systems that address this task should not just be accurate, but they should also be able to identify an interpretable discriminative structure which describes the most discriminant structural elements with respect to some target. The application of ILP in an interactive software for discriminative mining of chemical fragments is presented in this paper. In particular, it is described the coupling of an ILP system with a molecular visualisation software that allows a chemist to graphically control the search for interesting patterns in chemical fragments. Furthermore, we show how structural information, such as rings, functional groups such as carboxyls, amines, methyls, and esters, are integrated and exploited in the search.

Abstract

Abstract
This chapter introduces inductive logic programming (ILP) from the perspective of search algorithms in computer science. It first briefly considers the version spaces approach to induction, and then focuses on inductive logic programming: from its formal definition and main techniques and strategies, to priors used to restrict the search space and optimized sequential, parallel, and stochastic algorithms. The authors hope that this presentation of the theory and applications of inductive logic programming will help the reader understand the theoretical underpinnings of ILP, and also provide a helpful overview of the State-of-the-Art in the domain. © 2008, IGI Global.

Abstract
Progress in Prolog applications requires ever better performance and scalability from Prolog implementation technology. Most modern Prolog systems are emulator-based. Best performance thus requires both good emulator design and good memory performance. Indeed, Prolog applications can often spend hundreds of megabytes of data, but there is little work on understanding and quantifying the interactions between Prolog programs and the memory architecture of modern computers. In a previous study of Prolog systems we have shown through simulation that Prolog applications usually, but not always, have good locality, both for deterministic and non-deterministic applications. We also showed that performance may strongly depend on garbage collection and on database operations. Our analysis left two questions unanswered: how well do our simulated results holds on actual hardware, and how much did our results depend on a specific configuration? In this work we use several simulation parameters and profiling counters to improve understanding of Prolog applications. We believe that our analysis is of interest to any system implementor who wants to understand his or her own system's memory performance.

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).