Alberto Anguita

The ACGT Master Ontology and its applications - Towards an ontology-driven cancer research and management system

OBJECTIVE This paper introduces the objectives, methods and results of ontology development in the EU co-funded project Advancing Clinico-genomic Trials on Cancer-Open Grid Services for Improving Medical Knowledge Discovery (ACGT). While the available data in the life sciences has recently grown both in amount and quality, the full exploitation of it is being hindered by the use of different underlying technologies, coding systems, category schemes and reporting methods on the part of different research groups. The goal of the ACGT project is to contribute to the resolution of these problems by developing an ontology-driven, semantic grid services infrastructure that will enable efficient execution of discovery-driven scientific workflows in the context of multi-centric, post-genomic clinical trials. The focus of the present paper is the ACGT Master Ontology (MO). METHODS ACGT project researchers undertook a systematic review of existing domain and upper-level ontologies, as well as of existing ontology design software, implementation methods, and end-user interfaces. This included the careful study of best practices, design principles and evaluation methods for ontology design, maintenance, implementation, and versioning, as well as for use on the part of domain experts and clinicians. RESULTS To date, the results of the ACGT project include (i) the development of a master ontology (the ACGT-MO) based on clearly defined principles of ontology development and evaluation; (ii) the development of a technical infrastructure (the ACGT Platform) that implements the ACGT-MO utilizing independent tools, components and resources that have been developed based on open architectural standards, and which includes an application updating and evolving the ontology efficiently in response to end-user needs; and (iii) the development of an Ontology-based Trial Management Application (ObTiMA) that integrates the ACGT-MO into the design process of clinical trials in order to guarantee automatic semantic integration without the need to perform a separate mapping process.

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.

OntoDataClean: ontology-based integration and preprocessing of distributed data

Within the knowledge discovery in databases (KDD) process, previous phases to data mining consume most of the time spent analysing data. Few research efforts have been carried out in theses steps compared to data mining, suggesting that new approaches and tools are needed to support the preparation of data. As regards, we present in this paper a new methodology of ontology-based KDD adopting a federated approach to database integration and retrieval. Within this model, an ontology-based system called OntoDataClean has been developed dealing with instance-level integration and data preprocessing. Within the OntoDataClean development, a preprocessing ontology was built to store the information about the required transformations. Various biomedical experiments were carried out, showing that data have been correctly transformed using the preprocessing ontology. Although OntoDataClean does not cover every possible data transformation, it suggests that ontologies are a suitable mechanism to improve quality in the various steps of KDD processes.

Nanoinformatics: developing new computing applications for nanomedicine

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended “nanotype” to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others.

NCBI2RDF: Enabling Full RDF-Based Access to NCBI Databases

RDF has become the standard technology for enabling interoperability among heterogeneous biomedical databases. The NCBI provides access to a large set of life sciences databases through a common interface called Entrez. However, the latter does not provide RDF-based access to such databases, and, therefore, they cannot be integrated with other RDF-compliant databases and accessed via SPARQL query interfaces. This paper presents the NCBI2RDF system, aimed at providing RDF-based access to the complete NCBI data repository. This API creates a virtual endpoint for servicing SPARQL queries over different NCBI repositories and presenting to users the query results in SPARQL results format, thus enabling this data to be integrated and/or stored with other RDF-compliant repositories. SPARQL queries are dynamically resolved, decomposed, and forwarded to the NCBI-provided E-utilities programmatic interface to access the NCBI data. Furthermore, we show how our approach increases the expressiveness of the native NCBI querying system, allowing several databases to be accessed simultaneously. This feature significantly boosts productivity when working with complex queries and saves time and effort to biomedical researchers. Our approach has been validated with a large number of SPARQL queries, thus proving its reliability and enhanced capabilities in biomedical environments.

PubDNA Finder

SUMMARY PubDNA Finder is an online repository that we have created to link PubMed Central manuscripts to the sequences of nucleic acids appearing in them. It extends the search capabilities provided by PubMed Central by enabling researchers to perform advanced searches involving sequences of nucleic acids. This includes, among other features (i) searching for papers mentioning one or more specific sequences of nucleic acids and (ii) retrieving the genetic sequences appearing in different articles. These additional query capabilities are provided by a searchable index that we created by using the full text of the 176 672 papers available at PubMed Central at the time of writing and the sequences of nucleic acids appearing in them. To automatically extract the genetic sequences occurring in each paper, we used an original method we have developed. The database is updated monthly by automatically connecting to the PubMed Central FTP site to retrieve and index new manuscripts. Users can query the database via the web interface provided. AVAILABILITY PubDNA Finder can be freely accessed at http://servet.dia.fi.upm.es:8080/pubdnafinder

Biomedical image processing integration through INBIOMED: a web services-based platform

New biomedical technologies need to be integrated for research on complex diseases. It is necessary to combine and analyze information coming from different sources: genetic-molecular, clinical data and environmental risks. This paper presents the work carried on by the INBIOMED research network within the field of biomedical image analysis. The overall objective is to respond to the growing demand of advanced information processing methods for: developing analysis tools, creating knowledge structure and validating them in pharmacogenetics, epidemiology, molecular and image based diagnosis research environments. All the image processing tools and data are integrated and work within a web services-based application, the so called INBIOMED platform. Finally, several biomedical research labs offered real data and validate the network tools and methods in the most prevalent pathologies: cancer, cardiovascular and neurological. This work provides a unique biomedical information processing platform, open to the incorporation of data coming from other feature disease networks.

Data access and management in ACGT: tools to solve syntactic and semantic heterogeneities between clinical and image databases

Recent changes in data management within post-genomic clinical trials have emphasized the need for novel methods and tools to solve semantic and syntactic heterogeneities among distributed sources of information. ACGT is an Integrated Project funded by the European Commission that aims at building a GRID-based platform comprised by a set of tools to support multicentric post-genomic clinical trials on cancer. The main goal of ACGT is to provide seamless access to heterogeneous sources of information. For this purpose, two core tools were developed and included in the ACGT architecture: the ACGT Semantic Mediator (ACGT-SM), and the Data Access Wrappers (ACGT-DAWs). The ACGT-SM addresses semantics and schema integration, while the ACGT-DAWs cope with syntactic heterogeneities. Once the sources are bridged together, they can be seamlessly accessed using the RDQL query language.We tested our tools using a set of three relational and DICOM based image sources obtaining promising results.

Integration of Omics Data for Cancer Research

The development of high-throughput techniques for analyzing cell components has provided vast amounts of data in recent years. This development of gene-sequencing methods was followed by advances in techniques for analyzing and managing data from transcriptomes, proteomes, and other omics data. The so-called omics revolution has led to the development of numerous databases describing specific cell components. A recent study suggests that cell behavior cannot be modeled by analyzing its constituents separately, but rather calls for an integrative approach (Barabasi and Oltvai 2004). Thus, specialized techniques are being developed to integrate omics information. To enable new research avenues that can take advantage of and apply this information to new therapies – e.g. in cancer research – methods must be designed that provide a seamless integration of these new databases with classical clinical data.

A Machine Learning Approach to Identify Clinical Trials Involving Nanodrugs and Nanodevices from ClinicalTrials.gov

Background Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs—even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results. Methods We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset. Results and Conclusions The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy.