Abstract

Abstract
The amount of information available in the MEDLINE database makes it very hard for a researcher to retrieve a reasonable amount of relevant documents using a simple query language interface. Automatic Classification of documents may be a valuable technology to help reducing the amount of documents retrieved for each query. To accomplish this process it is of capital importance to use appropriate pre-processing techniques on the data. The main goal of this study is to analyse the impact of pre-processing techniques in text Classification of MEDLINE documents. We have assessed the effect of combining different pre-processing techniques together with several classification algorithms available in the WEKA tool. Our experiments show that the application of pruning, stemming and WordNet reduces significantly the number of attributes and improves the accuracy of the results.

Abstract

Abstract
This paper describes a shell for cooperating expert systems that has been developed at the University of Porto. The main goal of this shell is two-fold: to generate a community of cooperative knowledge-based systems and to develop several special reasoning techniques which can be used under a distributed and cooperative paradigm. UPShell is able to convert a set of generated intelligent systems (ISs) into a community of cooperative ISs. In this first version it is already possible to generate different intelligent systems which are able to run 'simultaneously' as separate Unix processes and, using a message-passing mechanism, to communicate among themselves. They can be set to pursue an overall goal in a cooperative way. Moreover, several tasks can be given to each IS to be solved simultaneously, and the IS can switch from task to task according to dynamic priorities reflecting the urgency attached to the specific sub-tasks that emerge. The shell described here may also be used to test, within a distributed environment, some time-bounded reasoning techniques that are presently being developed. The paper has three main parts: a general overview of the UPShell (Section 1); a tutorial explaining, by means of examples, how to use the package (Section 2); and, finally, some considerations on the reasoning techniques used and future improvements (Sections 3-5).

Abstract
The detailed study of folding and unfolding events in proteins is becoming central to develop rational therapeutic strategies against maladies such as Alzheimer and Parkinson disease. A promising approach to study the unfolding processes of proteins is through computer simulations. However, these computer simulations generate huge amounts of data that require computational methods for their analysis. In this paper we report on the use of Inductive Logic Programming (ILP) techniques to analyse the trajectories of protein unfolding simulations. The paper describes ongoing work on one of several problems of interest in the protein unfolding setting. The problem we address here is that of explaining what makes secondary structure elements to break down during the unfolding process. We tackle such problem collecting examples of contexts where secondary structures break and (automatically) constructing rules that may be used to suggest the explanations.

Abstract
IndLog is a general purpose Prolog-based Inductive Logic Programming (ILP) system. It is theoretically based on the Mode Directed Inverse Entailment and has several distinguishing features that makes it adequate for a wide range of applications. To search efficiently through large hypothesis spaces, IndLog uses original features like lazy evaluation of examples and Language Level Search. IndLog is applicable in numerical domains using the lazy evaluation of literals technique and Model Validation and Model Selection statistical-based techniques. IndLog has a MPI/LAM interface that enables its use in parallel or distributed environments, essential for Multi-relational Data Mining applications. Parallelism may be used in three flavours: splitting of the data among the computation nodes; parallelising the search through the hypothesis space and; using the different computation nodes to do theory-level search. IndLog has been applied successfully to major ILP literature datasets from the Life Sciences, Engineering, Reverse Engineering, Economics, Time-Series modelling to name a few.