Catálogo de publicaciones - libros

The mapping between databases and ontologies is an issue of importance for the creation of the Semantic Web. This is mainly due to the large amount of web data stored in databases. Our approach tackles the consideration of the dynamic aspects of relational databases in knowledge bases. This solution is of particular interest for “ontology-driven” information systems equipped with inference functionality and which require synchronization with legacy database.

Representing knowledge about researchers and research communities is a prime use case for distributed, locally maintained, interlinked and highly structured information in the spirit of the Semantic Web. In this paper we describe the publicly available ‘Semantic Web for Research Communities’ (SWRC) ontology, in which research communities and relevant related concepts are modelled. We describe the design decisions that underlie the ontology and report on both experiences with and known usages of the SWRC Ontology. We believe that for making the Semantic Web reality the re-usage of ontologies and their continuous improvement by user communities is crucial. Our contribution aims to provide a description and usage guidelines to make the value of the SWRC explicit and to facilitate its re-use.

The Workshop on Computational Methods in Bioinformatics was held in Covilhã between the 5th and 8th December 2005, as part of the 12th Portuguese Conference on Artificial Intelligence.

The success of bioinformatics in recent years has been prompted by research in molecular biology and molecular medicine in initiatives like the human genome project. These initiatives gave rise to an exponential increase in the volume and diversification of data, including protein and gene data, nucleotide sequences and biomedical literature. The accumulation and exploitation of large-scale data bases prompts for new computational technology and for research into these issues. In this context, many widely successful computational models and tools used by biologists in these initiatives, such as clustering and classification methods for gene expression data, are based on artificial intelligence (AI) techniques. Hence, this workshop brought the opportunity to discuss applications of AI with an interdisciplinary character, exploring the interactions between sub-areas of AI and Bioinformatics.

We tackle the problem of sequence classification using relevant subsequences found in a dataset of protein labelled sequences. A subsequence is if it is frequent and has a minimal length. For each query sequence a vector of features is obtained. The features consist in the number and average length of the relevant subsequences shared with each of the protein families. Classification is performed by combining these features in a Bayes Classifier. The combination of these characteristics results in a multi-class and multi-domain method that is exempt of data transformation and background knowledge. We illustrate the performance of our method using three collections of protein datasets. The performed tests showed that the method has an equivalent performance to state of the art methods in protein classification.

The amount of information about the genome, transcriptome and proteome, forms a problem for the scientific community: how to find the right information in a reasonable amount of time. Most research aiming to solve this problem, however, concentrate on a certain organism or a very limited dataset. Complementary to those algorithms, we developed CONAN, a system which provides a full-scale approach, tailored to experimentalists, designed to combine several information extraction methods and connect the outcome of these methods to gather novel information. Its methods include tagging of gene/protein names, finding interaction and mutation data, tagging of biological concepts, linking to MeSH and Gene Ontology terms, which can all be found back by querying the system. We present a full-scale approach that will ultimately cover all of PubMed/MEDLINE. We show that this universality has no effect on quality: our system performs as well as existing systems.

This paper describes a novel approach to deal with multiple sequence alignment (MSA). MSA is an essential task in bioinformatics which is at the heart of denser and more complex tasks in biological sequence analysis. MSA problem still attracts researcher’s attention despite the significant research effort spent to solve it. We propose in this paper a quantum evolutionary algorithm to improve solutions given by CLUSTALX package. The contribution consists in defining an appropriate representation scheme that allows applying successfully on MSA problem some quantum computing principles like qubit representation and superposition of states. This representation scheme is embedded within an evolutionary algorithm leading to an efficient hybrid framework which achieves better balance between exploration and exploitation capabilities of the search process. Experiments on a wide range of data sets have shown the effectiveness of the proposed framework and its ability to improve by many orders of magnitude the CLUSTALX’s solutions.

This paper investigates how predictive clustering trees can be used to predict gene function in the genome of the yeast . We consider the MIPS FunCat classification scheme, in which each gene is annotated with one or more classes selected from a given functional class hierarchy. This setting presents two important challenges to machine learning: (1) each instance is labeled with a set of classes instead of just one class, and (2) the classes are structured in a hierarchy; ideally the learning algorithm should also take this hierarchical information into account. Predictive clustering trees generalize decision trees and can be applied to a wide range of prediction tasks by plugging in a suitable distance metric. We define an appropriate distance metric for hierarchical multi-classification and present experiments evaluating this approach on a number of data sets that are available for yeast.

The 2005 EKDB&W – workshop objective was to attract contributions related to methods for nontrivial extraction of information from data. This book of proceedings includes 10 selected papers (resulting from 3 reviews). We believe that the diversity of these papers illustrates the EKDB & W objective attainment.

Unsupervised Learning (Clustering methods in particular) was addressed in 3 papers: (i) an extension of traditional SOM was proposed which considered specific measures of distance for categorical attributes; (ii) an empirical ranking of information criteria was provided for determining the number of clusters when dealing with mixed attributes in Latent Segments Models; (iii) CLOPE was found particularly useful to deal with binary basket data and provided the means to define web User Group Profiles. Supervised Learning was addressed in 3 papers: (i) multi-output nonparametric regression methods were presented comparing alternative ways to integrate co-response observations; (ii) Peepholing Techniques were adapted for Regression Trees, providing means to reduce the number of continuous variables and the ranges considered for nodes splitting; (iii) a Multi-Layer Perceptron was used to classify vector structures derived using the Law’s Algorithm. Three papers address the issue of data and knowledge extraction dealing directly with databases and data warehouses: (i) a methodology to evaluate the quality of Meta-Data describing contents in web portals was proposed; (ii) a new approach to retrieve data from semi-structured text .les and integrate it in a decision support system was proposed; (iii) an alternative approach for itemset mining over large transactional tables was presented. Finally, an alternative approach to the L* algorithm was proposed, trying to diminish the needless repetition of membership queries.

Application domains were very diverse and illustrated the practical utility of the presented methodologies. They ranged from Web services and Retail to the treatment of Sea surface data, Space Weather and Spacecraft data. Papers in these areas hopefully contributed bridge the gap between research and practice. The EKDB & W Workshop would not have been possible without the contribution of Authors, Program Committee members and EPIA 2005 Organizers. All deserve our thanks and appreciation.

Several non-parametric regression methods with various dependent variables that are possibly related are explored. The techniques which produce the best results in the simulations are those which incorporate the observations of the other response variables in the estimator. Compared to analogous single-response techniques, this approach results in a significant reduction in the quadratic error in the response.

This paper presents an adaptation of the peepholing method to regression trees. Peepholing was described as a means to overcome the major computational bottleneck of growing classification trees by Catlett [3] . This method involves two major steps: shortlisting and blinkering. The former has the goal of eliminating some continuous variables from consideration when growing the tree, while the second tries to restrict the range of values of the remaining continuous variables that should be considered when searching for the best cut point split. Both are effective means of overcoming the most costly step of growing tree-based models: sorting the values of the continuous variables before selecting their best split. In this work we describe the adaptations that are necessary to use this method within regression trees. The major adaptations involve developing means to obtain biased estimates of the criterion used to select the best split of these models. We present some preliminary experiments that show the effectiveness of our proposal.