Catálogo de publicaciones - libros

Influenza is the last of the classic plagues of the past, which still has to be brought under control. It causes a lot of costs: prolonged stays in hospitals and especially many days of unfitness for work. Therefore many of the most developed countries have started to create influenza surveillance systems. Mostly statistical methods are applied to predict influenza epidemics. However, the results are rather moderate, because influenza waves occur in irregular cycles. We have developed a method that combines Case-Based Reasoning with temporal abstraction. Here we compare experimental results of our method and statistical methods.

The effectiveness of cancer treatment depends strongly on an accurate diagnosis. In this paper we propose a system for automatic and precise diagnosis of a tumor’s origin based on genetic data. This system is based on a combination of coding theory techniques and machine learning algorithms. In particular, tumor classification is described as a multiclass learning setup, where gene expression values serve the system to distinguish between types of tumors. Since multiclass learning is intrinsically complex, the data is divided into several biclass problems whose results are combined with an error correcting linear block code. The robustness of the prediction is increased as errors of the base binary classifiers are corrected by the linear code. Promising results have been achieved with a best case precision of 72% when the system was tested on real data from cancer patients.

The inductive learning algorithms are the very attractive methods generating hierarchical classifiers. They generate hypothesis of the target concept on the base on the set of labeled examples. This paper presents some of the decision tree induction methods, boosting concept and their usefulness for diagnosis of the type of hypertension (essential hypertension and five type of secondary one: fibroplastic renal artery stenosis, atheromatous renal artery stenosis, Conn’s syndrome, renal cystic disease and pheochromocystoma). The decision on the type of hypertension is made only on base on blood pressure, general information and basis biochemical data.

In many medical decision problems there exist dependencies between subsequent diagnosis of the same patient. Among the different concepts and methods of using “contextual” information in pattern recognition, the approach through Bayes compound decision theory is both attractive and efficient from the theoretical and practical point of view. Paper presents the probabilistic approach (based on expert rules and learning set) to the problem of recognition of state of acid-base balance and to the problem of computer-aided anti-hypertension drug therapy. The quality of obtained classifier are compared to the frquencies of correct classification of three neural nets.

A specific feature of the explored diagnosis task is the dependence between patient’s states at particular instants, which should be taken into account in sequential diagnosis algorithms. In this paper methods for performing sequential diagnosis using fuzzy relation in product of diagnoses set and fuzzified feature space are developed and evaluated. In the proposed method first on the base of learning set fuzzy relation is determined as a solution of appropriate optimization problem and next this relation in the form of matrix of membership grade values is used at successive instants of sequential diagnosis process. Different algorithms of sequential diagnosis which differ with as well the sets of input data as procedure are described. Proposed algorithms were practically applied to the computer-aided recognition of patient’s acid-base equilibrium states where as an optimization procedure genetic algorithm was used. Results of comparative experimental analysis of investigated algorithms in respect of classification accuracy are also presented and discussed.

Following a systems engineering approach we have identified the information system requirements for biomedical collaborative research. We have designed a Service Oriented Architecture following a dynamic and adaptable to change approach, using technology and specifications that are being developed in an open way, utilizing industry partnerships and broad consortia such as W3C and the Organization for the Advancement of Structured Information Standards (OASIS), and based on standards and technology that are the foundation of the Internet. The design has been translated in a pilot implementation infrastructure (INBIOMED) that is now been populated with web services for data and images analysis and collaborative management.

One of the first motivations of using grids comes from applications managing large data sets like for example in High Energy Physic or Life Sciences. To improve the global throughput of software environments, replicas are usually put at wisely selected sites. Moreover, computation requests have to be scheduled among the available resources. To get the best performance, scheduling and data replication have to be tightly coupled which is not always the case in existing approaches.

This paper presents an algorithm that combines data management and scheduling at the same time using a steady-state approach. Our theoretical results are validated using simulation and logs from a large life science application (ACI GRID GriPPS). The PattInProt application searches sites and signatures of proteins into databanks of protein sequences.

Enhancing training and mobility in the area of Biomedical Informatics (BMI) is one of the most important objectives of the European Network of Excellence INFOBIOMED. Based on the lessons learned from previous decades of experiences in teaching Medical Informatics and Bioinformatics, an action plan has been elaborated. This plan is structured into three actions: (a) a survey to analyze and evaluate the situation, needs and expectations in BMI. (b) A Biomedical Informatics course database (ICD) containing the relevant keywords in the area, and (c) the design and implementation of a Mobility Brokerage Service (MBS) to enhance mobility and exchanges in the area. This paper describes the overall approach and technical characteristics of the MBS. It follows an innovative service-oriented architecture based on Web Services, providing distributed access to on-line information sources. This approach is being evaluated and reused for different research applications within the Network.

Over recent years the field of phylogenetics has witnessed significant algorithmic and technical progress. A new class of efficient phylogeny programs allows for computation of large evolutionary trees comprising 500–1.000 organisms within a couple of hours on a single CPU under elaborate optimization criteria. However, it is difficult to extract the valuable information contained in those large trees without appropriate visualization tools. As potential solution we propose the application of treemaps to visualize large phylogenies (evolutionary trees) and improve knowledge-retrieval. In addition, we propose a hybrid tree/treemap representation which provides a detailed view of subtrees via treemaps while maintaining a contextual view of the entire topology at the same time. Moreover, we demonstrate how it can be deployed to visualize an evolutionary tree comprising 2.415 mammals. The respective software package is available on-line at www.ics.forth.gr/~stamatak.

Current approaches using Artificial Intelligence techniques applied to chemistry use representations inherited from existing tools. These tools describe chemical compounds with a set of structure-activity relationship (SAR) descriptors because they were developed mainly for the task of drug design. We propose an ontology based on the chemical nomenclature as a way to capture the concepts commonly used by chemists in describing molecular structure of the compounds. In this paper we formally specify the concepts and relationships of the chemical nomenclature in a comprehensive ontology using a form of relational representation called . We also provide several examples of describing chemical compounds using this ontology and compare our proposal with other SAR based approaches.