Abstract
The rapid growth of the scientific literature makes text classification essential specially in the biomedical research domain to help researchers to focus on the latest findings in a fast and efficient way. The potential benefits of using text semantic enrichment to enhance the biomedical document classification is presented in this study. We show the importance of enriching the corpora with semantic information to improve the full-text classification. The approach involves the semantic enrichment of a Medline corpus with a Semantic Repository (SemRep) which extracts semantic predications from biomedical text. The study also addresses the problem of treating highly dimensional data while maintaining the semantic structure of the corpus. Experimental results lead to the sustained conclusion that better results are achieved with full-text instead of using only abstracts and titles. We also conclude that the application of enriched techniques to full-texts significantly improves the task of text classification providing a significant contribution for the biomedical text mining research.

Abstract

Abstract
The work reported in this article addresses the challenge of building models for non-trivial aerobatic aircraft maneuvers in an automated fashion. It is built using a Behavioural Cloning approach where human pilots provide a set of example maneuvers used by a Machine Learning algorithm to induce a control model for each maneuver. The best examples for each maneuver were selected using a set of objective evaluation metrics. Using those example sets, robust models were induced that could replicate (and in some cases outperform) the human pilots that provided the examples (the clean-up effect). Complete complex maneuvers were performed using a meta-controller capable of sequencing the basic ones learned by imitation. This endeavor was rewarded by the results that show several Machine Learning models capable of performing highly complex aircraft maneuvers.

Abstract
This article reports on the development of a Web platform for the study of Adverse Drug Events (ADEs). The platform is able to import ADE episodes from official Web sites, like OpenFDA, analyse the chemistry of the drugs involved, together with patient data, and produce a potential explanation based on the drugs interactions. Each study uses chemical knowledge to enrich the information on the molecules involved in the episodes. Data Mining is then used to construct models that can help in the explanation of the ADE occurrence and to predict future events. This paper reports on the Web portal developed and the Data Mining experiments conducted to evaluate the quality, and potential explanations of the forecasted adverse reactions, using real reports of drug administration and the subsequent adverse events. The results showed that it was possible to predict the outcomes of ADEs based on the structure of the molecules of the drugs involved and the data collected from real reports of drug administration up to an accuracy of 79%, while also predicting, with high accuracy, the severity of events where the outcome is the death of the patient (with a precision of 98.9%). The platform provides a less expensive and more accurate way of predicting adverse drug reactions compared to traditional methods. This study highlights the importance of understanding drug interactions at a molecular level and the usefulness of utilising Data Mining techniques in predicting ADEs.

Abstract

Abstract
Whenever new sequences of DNA or proteins have been decoded it is almost compulsory to look at similar sequences and papers describing those sequences in order to both collect relevant information concerning the function and activity of the new sequences and/or know what is known already about similar sequences that might be useful in the explanation of the function or activity of the newly discovered ones. In current web sites and data bases of sequences there are, usually, a set of paper references linked to each sequence. Those links are very useful because the papers describe useful information concerning the sequences. They are, therefore, a good starting point to look for relevant information related to a set of sequences. One way is to implement such approach is to do a blast with the new decoded sequences, and collect similar sequences. Then one looks at the papers linked with the similar sequences. Most often the number of retrieved papers is small and one has to search large data bases for relevant papers. In this paper we propose a process of generating a classifier based on the initially set of relevant papers that are directly linked to the similar sequences retrieved and use that classifier to automatically enlarge the set of relevant papers by searching the MEDLINE using the automatically constructed classifier. We have empirically evaluated our proposal and report very promising results. © 2011 IEEE.