Catálogo de publicaciones - libros

Data integration is an important part of biological applications that acquire data generated using evolving technologies and methods or involve data analysis across diverse specialized databases that reflect the expertise of different groups in a specific domain. The increasing number of such databases, the emergence of new types of data that need to be captured, as well as the evolving biological knowledge add to the complexity of already challenging integration problems. Furthermore, devising solutions to these problems requires technical expertise in several areas, such as database management systems, database administration and software engineering, as well as data modeling and analysis.

Many countries around the world have placed an increased focus on the need to modernize their healthcare information infrastructure. This is particularly challenging in the United States. The U.S. healthcare industry is by far the largest in the world in both absolute dollars and in percentage of GDP (>$1.7T – 15% of GDP). It is also quite fragmented and complex. This complexity, coupled with an antiquated infrastructure for the collection of and access to medical data, leads to enormous inefficiencies and sources of error. Driven by consumer, regulatory, and governmental pressure, there is a growing consensus that the time has come to modernize the US Healthcare Information Infrastructure (HII). A modern HII will provide care givers with better and timelier access to data. The launch of a National Health Infrastructure Initiative (NHII) in the US in May 2004 – with the goal of providing an electronic health record for every American within the next decade- will eventually transform the healthcare industry in general...just as I/T has transformed other industries in the past. While such transformation may be disruptive in the short term, it will in the future significantly improve the quality, efficiency, and successful delivery of healthcare while decreasing costs to patients and payers and improving the overall experiences of consumers and providers. The key to this successful outcome will be based on the way we apply I/T to healthcare data and to the services delivered through that I/T. This must be accomplished in a way that protects individuals, allows competition, but gives caregivers reliable and efficient access to the data required to treat patients and to improve the practice of medical science.

Grid computing has great potential for supporting the integration of complex, fast changing biological data repositories to enable distributed data analysis. One scenario where Grid computing has such potential is provided by proteomics resources which are rapidly being developed with the emergence of affordable, reliable methods to study the proteome. The protein identifications arising from these methods derive from multiple repositories which need to be integrated to enable uniform access to them. A number of technologies exist which enable these resources to be accessed in a Grid environment, but the independent development of these resources means that significant data integration challenges, such as heterogeneity and schema evolution, have to be met. This paper presents an architecture which supports the combined use of Grid data access (OGSA-DAI), Grid distributed querying (OGSA-DQP) and data integration (AutoMed) software tools to support distributed data analysis. We discuss the application of this architecture for the integration of several autonomous proteomics data resources.

The goal of the EU FP6 project DIAMONDS is to build a computational platform for studying the cell-cycle regulation process in several different (model) organisms (S. cerevisiae, S. pombe, A. thaliana and human). This platform will enable wet-lab biologists to use a systems biology approach encompassing data integration, modeling and simulation, thereby supporting analysis and interpretation of biochemical pathways involved in the cell cycle. To facilitate the computational handling of cell-cycle specific knowledge a detailed cell-cycle ontology is essential. The currently existing cell-cycle branch of the Gene Ontology (GO) provides only a static view and it is not rich enough to support in-depth cell-cycle studies.

In this work, an enhanced Cell-Cycle Ontology (CCO) is proposed as an extension to existing GO. Besides the classical add-ons given by an ontology (data repository, knowledge sharing, validation, annotation, and so on), CCO is intended to further evolve into a knowledge-based system that provides reasoning services oriented to hypotheses evaluation in the context of cell-cycle studies. A data integration pipeline prototype, covering the entire life cycle of the knowledge base, is presented. Concrete problems and initial results related to the implementation of automatic format mappings between ontologies and inconsistency checking issues are discussed in detail.

Public biological databases contain vast amounts of rich data that can also be used to create and evaluate new biological hypothesis. We propose a method for link discovery in biological databases, i.e., for prediction and evaluation of implicit or previously unknown connections between biological entities and concepts. In our framework, information extracted from available databases is represented as a graph, where vertices correspond to entities and concepts, and edges represent known, annotated relationships between vertices. A link, an (implicit and possibly unknown) relation between two entities is manifested as a path or a subgraph connecting the corresponding vertices. We propose measures for link goodness that are based on three factors: edge reliability, relevance, and rarity. We handle these factors with a proper probabilistic interpretation. We give practical methods for finding and evaluating links in large graphs and report experimental results with Alzheimer genes and protein interactions.

An essential part of bioinformatic research concerns the iterative process of validating hypotheses by analyzing facts stored in databases and in published literature. This process can be enhanced by language technology methods, in particular by automatic text understanding. Since it is becoming increasingly difficult to keep up with the vast number of scientific articles being published, there is a need for more easily accessible representations of the current knowledge. The goal of the research described in this paper is to develop a system aimed to support the large-scale research on metabolic and regulatory pathways by extracting relations between biological objects from descriptions found in literature. We present and evaluate the procedures for semantico-syntactic tagging, dividing the text into parts concerning previous research and current research, syntactic parsing, and transformation of syntactic trees into logical representations similar to the pathway graphs utilized in the Kyoto Encyclopaedia of Genes and Genomes.

Linking the biomedical literature to other data resources is notoriously difficult and requires text mining. Text mining aims to automatically extract facts from literature. Since authors write in natural language, text mining is a great natural language processing challenge, which is far from being solved. We propose an alternative: If authors and editors summarize the main facts in a controlled natural language, text mining will become easier and more powerful. To demonstrate this approach, we use the language Attempto Controlled English (ACE). We define a simple model to capture the main aspects of protein interactions. To evaluate our approach, we collected a dataset of 459 paragraph headings about protein interaction from literature. 56% of these headings can be represented exactly in ACE and another 23% partially. These results indicate that our approach is feasible.

Pharmacogenomics explores the impact of individual genomic variations in health problems such as adverse drug reactions. Records of millions of genomic variations, mostly known as Single Nucleotide Polymorphisms (SNP), are available today in various overlapping and heterogeneous databases. Selecting and extracting from these databases or from private sources a proper set of polymorphisms are the first steps of a KDD (Knowledge Discovery in Databases) process in pharmacogenomics. It is however a tedious task hampered by the heterogeneity of SNP nomenclatures and annotations. Standards for representing genomic variants have been proposed by the Human Genome Variation Society (HGVS). The SNP-Converter application is aimed at converting any SNP description into an HGVS-compliant pivot description and vice versa. Used in the frame of a knowledge system, the SNP-Converter application contributes as a wrapper to semantic data integration and enrichment.

Simulating networks of biochemical reactions require reliable kinetic data. In order to facilitate the access to such kinetic data we have developed SABIO-RK, a curated database with information about biochemical reactions and their kinetic properties. The data are manually extracted from literature and verified by curators, concerning standards, formats and controlled vocabularies. This process is supported by tools in a semi-automatic manner. SABIO-RK contains and merges information about reactions such as reactants and modifiers, organism, tissue and cellular location, as well as the kinetic properties of the reactions. The type of the kinetic mechanism, modes of inhibition or activation, and corresponding rate equations are presented together with their parameters and measured values, specifying the experimental conditions under which these were determined. Links to other databases enable the user to gather further information and to refer to the original publication. Information about reactions and their kinetic data can be exported to an SBML file, allowing users to employ the information as the basis for their simulation models.

The recent technological advancements in biological research have allowed researchers to advance their knowledge of the domain far beyond expectations. The advent of easily accessible biological web databases such as NCBI databases and associated tools such as BLAST are key components to this development. However, with the growing number of these web based biological research tools and data sources, the time necessary to invest in becoming a domain expert is immense. Therefore, it is important to allow for easy user deployment of the wealth of available data sources and tools necessary to conduct biological research. In this paper we discuss an approach to create and maintain a robust ontology knowledge base that serves as the core for SIBIOS, a workflow based integration system for bioinformatics tools and data sources. Further, deployment of the ontology in various components of SIBIOS is discussed.