Victor Maojo

Medical Data Analysis

In its first part, this contribution reviews shortly the application of neural network methods to medical problems and characterizes its advantages and problems in the context of the medical background. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic systems. Then, paradigm of neural networks is shortly introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. Additionally, the problem of interfacing the network and its result is given and the neurofuzzy approach is presented. Finally, as case study of neural rule based diagnosis septic shock diagnosis is described, on one hand by a growing neural network and on the other hand by a rule based system.

Biological and Medical Data Analysis

Medical Databases and Information Systems.- Application of Three-Level Handprinted Documents Recognition in Medical Information Systems.- Data Management and Visualization Issues in a Fully Digital Echocardiography Laboratory.- A Framework Based on Web Services and Grid Technologies for Medical Image Registration.- Biomedical Image Processing Integration Through INBIOMED: A Web Services-Based Platform.- The Ontological Lens: Zooming in and out from Genomic to Clinical Level.- Data Analysis and Image Processing.- Dynamics of Vertebral Column Observed by Stereovision and Recurrent Neural Network Model.- Endocardial Tracking in Contrast Echocardiography Using Optical Flow.- Unfolding of Virtual Endoscopy Using Ray-Template.- Knowledge Discovery and Data Mining.- Integration of Genetic and Medical Information Through a Web Crawler System.- Vertical Integration of Bioinformatics Tools and Information Processing on Analysis Outcome.- A Grid Infrastructure for Text Mining of Full Text Articles and Creation of a Knowledge Base of Gene Relations.- Prediction of the Performance of Human Liver Cell Bioreactors by Donor Organ Data.- A Bioinformatic Approach to Epigenetic Susceptibility in Non-disjunctional Diseases.- Foreseeing Promising Bio-medical Findings for Effective Applications of Data Mining.- Statistical Methods and Tools for Biomedical Data Analysis.- Hybridizing Sparse Component Analysis with Genetic Algorithms for Blind Source Separation.- Hardware Approach to the Artificial Hand Control Algorithm Realization.- Improving the Therapeutic Performance of a Medical Bayesian Network Using Noisy Threshold Models.- SVM Detection of Premature Ectopic Excitations Based on Modified PCA.- Decision Support Systems.- A Text Corpora-Based Estimation of the Familiarity of Health Terminology.- On Sample Size and Classification Accuracy: A Performance Comparison.- Influenza Forecast: Comparison of Case-Based Reasoning and Statistical Methods.- Tumor Classification from Gene Expression Data: A Coding-Based Multiclass Learning Approach.- Boosted Decision Trees for Diagnosis Type of Hypertension.- Markov Chains Pattern Recognition Approach Applied to the Medical Diagnosis Tasks.- Computer-Aided Sequential Diagnosis Using Fuzzy Relations - Comparative Analysis of Methods.- Collaborative Systems in Biomedical Informatics.- Service Oriented Architecture for Biomedical Collaborative Research.- Simultaneous Scheduling of Replication and Computation for Bioinformatic Applications on the Grid.- The INFOBIOMED Network of Excellence: Developments for Facilitating Training and Mobility.- Bioinformatics: Computational Models.- Using Treemaps to Visualize Phylogenetic Trees.- An Ontological Approach to Represent Molecular Structure Information.- Focal Activity in Simulated LQT2 Models at Rapid Ventricular Pacing: Analysis of Cardiac Electrical Activity Using Grid-Based Computation.- Bioinformatics: Structural Analysis.- Extracting Molecular Diversity Between Populations Through Sequence Alignments.- Detection of Hydrophobic Clusters in Molecular Dynamics Protein Unfolding Simulations Using Association Rules.- Protein Secondary Structure Classifiers Fusion Using OWA.- Efficient Computation of Fitness Function by Pruning in Hydrophobic-Hydrophilic Model.- Evaluation of Fuzzy Measures in Profile Hidden Markov Models for Protein Sequences.- Bioinformatics: Microarray Data Analysis.- Relevance, Redundancy and Differential Prioritization in Feature Selection for Multiclass Gene Expression Data.- Gene Selection and Classification of Human Lymphoma from Microarray Data.- Microarray Data Analysis and Management in Colorectal Cancer.

A context vector model for information retrieval

In the vector space model for information retrieval, term vectors are pair-wise orthogonal, that is, terms are assumed to be independent. It is well known that this assumption is too restrictive. In this article, we present our work on an indexing and retrieval method that, based on the vector space model, incorporates term dependencies and thus obtains semantically richer representations of documents. First, we generate term context vectors based on the co-occurrence of terms in the same documents. These vectors are used to calculate context vectors for documents. We present different techniques for estimating the dependencies among terms. We also define term weights that can be employed in the model. Experimental results on four text collections (MED, CRANFIELD, CISI, and CACM) show that the incorporation of term dependencies in the retrieval process performs statistically significantly better than the classical vector space model with IDF weights. We also show that the degree of semantic matching versus direct word matching that performs best varies on the four collections. We conclude that the model performs well for certain types of queries and, generally, for information tasks with high recall requirements. Therefore, we propose the use of the context vector model in combination with other, direct word-matching methods.

Nanoinformatics and DNA-based computing: catalyzing nanomedicine.

Five decades of research and practical application of computers in biomedicine has given rise to the discipline of medical informatics, which has made many advances in genomic and translational medicine possible. Developments in nanotechnology are opening up the prospects for nanomedicine and regenerative medicine where informatics and DNA computing can become the catalysts enabling health care applications at sub-molecular or atomic scales. Although nanomedicine promises a new exciting frontier for clinical practice and biomedical research, issues involving cost-effectiveness studies, clinical trials and toxicity assays, drug delivery methods, and the implementation of new personalized therapies still remain challenging. Nanoinformatics can accelerate the introduction of nano-related research and applications into clinical practice, leading to an area that could be called “translational nanoinformatics.” At the same time, DNA and RNA computing presents an entirely novel paradigm for computation. Nanoinformatics and DNA-based computing are together likely to completely change the way we model and process information in biomedicine and impact the emerging field of nanomedicine most strongly. In this article, we review work in nanoinformatics and DNA (and RNA)-based computing, including applications in nanopediatrics. We analyze their scientific foundations, current research and projects, envisioned applications and potential problems that might arise from them.

On distributing load in cloud computing: A real application for very-large image datasets

Managing large image collections has become an important issue for information companies and institutions. We present a cloud computing service and its application for the storage and analysis of very-large images. This service has been implemented using multiple distributed and collaborative agents. For image storage and analysis, a regionoriented data structure is utilized, which allows storing and describing image regions using low-level descriptors. Different types of structural relationships between regions are also taken into account. A distinctive goal of this work is that data operations are adapted for working in a distributed mode. This allows that an input image can be divided into different sub-images that can be stored and processed separately by different agents in the system, facilitating processing very-large images in a parallel manner. A key aspect to decrease processing time for parallelized tasks is the use of an appropriate load balancer to distribute and assign tasks to agents with less workload.

Improving data and knowledge management to better integrate health care and research

This review is based on the debates held in Barcelona from 3 July 2012 to 4 July 2012 with the active participation of all authors. The debates were organized by B-Debate (an initiative of Biocat and Obra Social ‘La Caixa’) and Universitat Pompeu Fabra (Barcelona). The event was held within the framework of the European INBIOMEDvision project (funded by the EU Seventh Framework Programme for Research and Technological Development (FP7) under grant agreement no. 270107). In addition, we received support from EU FP7 project no. 200754 (GEN2PHEN) and the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115002 (eTOX) and no. 115191 (Open PHACTS), resources of which are composed of financial contribution from the EU FP7 and in kind contributions from companies of the European Federation of Pharmaceutical Industries and Associations. L.I.F received support from Instituto de Salud Carlos III Fondo Europeo de Desarollo Regional (CP10/00524).

Design of Virtual Reality Systems for Education: A Cognitive Approach

One of the main problems with virtual reality as a learning tool is that there are hardly any theories or models upon which to found and justify the application development. This paper presents a model that defends the metaphorical design of educational virtual reality systems. The goal is to build virtual worlds capable of embodying the knowledge to be taught: the metaphorical structuring of abstract concepts looks for bodily forms of expression in order to make knowledge accessible to students. The description of a case study aimed at learning scientific categorization serves to explain and implement the process of metaphorical projection. Our proposals are based on Lakoff and Johnsons theory of cognition, which defends the conception of the embodied mind, according to which most of our knowledge relies on basic metaphors derived from our bodily experience.

BIRI: a new approach for automatically discovering and indexing available public bioinformatics resources from the literature

BackgroundThe rapid evolution of Internet technologies and the collaborative approaches that dominate the field have stimulated the development of numerous bioinformatics resources. To address this new framework, several initiatives have tried to organize these services and resources. In this paper, we present the BioInformatics Resource Inventory (BIRI), a new approach for automatically discovering and indexing available public bioinformatics resources using information extracted from the scientific literature. The index generated can be automatically updated by adding additional manuscripts describing new resources. We have developed web services and applications to test and validate our approach. It has not been designed to replace current indexes but to extend their capabilities with richer functionalities.ResultsWe developed a web service to provide a set of high-level query primitives to access the index. The web service can be used by third-party web services or web-based applications. To test the web service, we created a pilot web application to access a preliminary knowledge base of resources. We tested our tool using an initial set of 400 abstracts. Almost 90% of the resources described in the abstracts were correctly classified. More than 500 descriptions of functionalities were extracted.ConclusionThese experiments suggest the feasibility of our approach for automatically discovering and indexing current and future bioinformatics resources. Given the domain-independent characteristics of this tool, it is currently being applied by the authors in other areas, such as medical nanoinformatics. BIRI is available at http://edelman.dia.fi.upm.es/biri/.

An Expert System for Managing Medical Appropriateness Criteria Based on Computer Algebra Techniques

Abstract This article presents the development of an expert system for managing medical appropriateness criteria together with an outline of its theoretical foundations. Techniques borrowed from computer algebra (Grobner bases) are applied to this field of medicine. The steps of the expert system construction process are as follows. First, the knowledge provided in table format by experts in coronary diseases is translated into a set of production rules of a rule-based expert system (RBES). Kleenes three-valued logic augmented with modal operators is chosen in order to manage uncertainty.

Nanoinformatics: a new area of research in nanomedicine

Over a decade ago, nanotechnologists began research on applications of nanomaterials for medicine. This research has revealed a wide range of different challenges, as well as many opportunities. Some of these challenges are strongly related to informatics issues, dealing, for instance, with the management and integration of heterogeneous information, defining nomenclatures, taxonomies and classifications for various types of nanomaterials, and research on new modeling and simulation techniques for nanoparticles. Nanoinformatics has recently emerged in the USA and Europe to address these issues. In this paper, we present a review of nanoinformatics, describing its origins, the problems it addresses, areas of interest, and examples of current research initiatives and informatics resources. We suggest that nanoinformatics could accelerate research and development in nanomedicine, as has occurred in the past in other fields. For instance, biomedical informatics served as a fundamental catalyst for the Human Genome Project, and other genomic and –omics projects, as well as the translational efforts that link resulting molecular-level research to clinical problems and findings.