Abstract Accurate crop yield forecasting requires identifying informative predictors from high-dimensional meteorological time series with multiple time lags. Such settings often lead to overfitting, reduced interpretability, and increased computational cost. This paper proposes a two-phase feature-selection framework, termed CS $$_\text {MRMR}$$ MRMR -DWES $$_R$$ R , to address these challenges in agricultural time-series regression. In the first phase, a supervised filter ranks features using correlation statistics with a maximum relevance-minimum redundancy criterion, retaining a compact subset of informative variables. In the second phase, a wrapper-based discrete weighted evolutionary strategy automatically selects the optimal feature subset by directly optimizing the predictive performance of a SARIMAX regression model. The proposed framework is evaluated on 20 years of monthly avocado and mango yield data from the Axarquía region of Málaga in Spain under multiple lag configurations. This feature selection might slightly change over time and depends entirely on the crop type and geographical location. The proposed CS $$_\text {MRMR}$$ MRMR -DWES $$_\text {R}$$ R framework inherently requires high-performance computing (HPC) capabilities due to the scale and complexity of the agricultural time-series datasets under study. Experimental results show that CS $$_\text {MRMR}$$ MRMR -DWES $$_R$$ R improves forecasting accuracy while reducing feature dimensionality and computational cost compared with baseline and existing feature-selection methods. These findings highlight the effectiveness of the proposed approach for high-dimensional agricultural time-series analysis.

Late detection of plant diseases leads to irreparable losses for farmers, threatening global food security, economic stability, and environmental sustainability. This research introduces the Multi-Objective Genetic Algorithm Explainer (MOGAE), a novel model-agnostic local explainer for image data aimed at the early detection of citrus diseases. MOGAE enhances eXplainable Artificial Intelligence (XAI) by leveraging the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an adaptive Bit Flip Mutation (BFM) incorporating densify and sparsify operators to adjust superpixel granularity automatically. This innovative approach simplifies the explanation process by eliminating several critical hyperparameters required by traditional methods like Local Interpretable Model-Agnostic Explanations (LIME). To develop the citrus disease classification model, we preprocess the leaf dataset through stratified data splitting, oversampling, and augmentation techniques, then fine-tuning a pre-trained Residual Network 50 layers (ResNet50) model. MOGAE’s effectiveness is demonstrated through comparative analyses with the Ensemble-based Genetic Algorithm Explainer (EGAE) and LIME, showing superior accuracy and interpretability using criteria such as numeric accuracy of explanation and Number of Function Evaluations (NFE). We assess accuracy both intuitively and numerically by measuring the Euclidean distance between expert-provided explanations and those generated by the explainer. The appendix also includes an extensive evaluation of MOGAE on the melanoma dataset, highlighting its versatility and robustness in other domains. The related implementation code for the fine-tuned ResNet50 and MOGAE is available at https://github.com/KhaosResearch/Plant-disease-explanation . • MOGAE eliminates several critical hyperparameters LIME requires, simplifying the explanation process. • MOGAE delivers more accurate and interpretable explanations than LIME, enhancing the reliability of the model’s decisions. • The integration of adaptive Bit Flip Mutation with densify and sparsify operators in NSGA-II significantly improves the algorithm’s exploration capabilities within MOGAE. • Comprehensive evaluations of MOGAE are conducted using both the citrus leaf diseases dataset and the melanoma dataset, demonstrating its versatility and effectiveness across different domains.

Developing an annual and global high-resolution land cover map is one of the most ambitious tasks in remote sensing, with increasing importance due to the continual rise in validated data and satellite imagery. The success of land cover classification models largely hinges on the data quality, coupled with the application of Big Data techniques and distributed computing. This is essential for efficiently processing the extensive volume of available satellite data. However, maintaining the lifecycle of several annual Machine Learning models presents a complex challenge. The rise of Machine Learning Operations offers an opportunity to automate the maintenance of these models, a feature particularly beneficial in systems that require generating new models each year alongside the continuous integration of validated data. This article details the development of an end-to-end MLOps workflow, meticulously integrating land cover classification models that employ Big Data strategies for processing large-scale, high-resolution spatial data. The workflow is designed within a Kubernetes environment, achieving on-demand auto-scaling, distributed computing, and load balancing. This integration demonstrates the practicality and efficiency of managing and deploying models that treat satellite imagery in an automated, scalable framework, thus marking a significant advancement in remote sensing and MLOps.

Feature selection has a prominent role in high-dimensional datasets to increase classification accuracy, decrease the learning algorithm computational time, and present the most informative features to decision-makers. This paper proposes a two-stage hybrid feature selection for high dimensional medical datasets: Maximum Pattern Recognition - Multi-objective Discrete Evolution Strategy (MPR-MDES). MPR is a rapid filter ranker that significantly outperforms existing frequency-based rankers in recognizing non-linear patterns, effectively eliminating a majority of non-informative features. Then, the wrapper Multi-objective Discrete Evolution Strategy(MDES) uses the remaining features and obtains sets of solutions which are automatically presented to decision-makers.The experiments conducted on large medical datasets demonstrate that MPR-MDES achieves considerable improvements compared to state-of-the-art methods, in terms of both classification accuracy and dimensionality reduction. In this sense, the proposal successfully performs when presenting informative feature sets to decision-makers. The implementation is available on https://github.com/KhaosResearch/MPR-MDES.

The application of semantic technologies, particularly ontologies, in the realm of multi-objective evolutionary algorithms is overlook despite their effectiveness in knowledge representation. In this paper, we introduce MOODY, an ontology specifically tailored to formalize these kinds of algorithms, encompassing their respective parameters, and multi-objective optimization problems based on a characterization of their search space landscapes. MOODY is designed to be particularly applicable in automatic algorithm configuration, which involves the search of the parameters of an optimization algorithm to optimize its performance. In this context, we observe a notable absence of standardized components, parameters, and related considerations, such as problem characteristics and algorithm configurations. This lack of standardization introduces difficulties in the selection of valid component combinations and in the re-use of algorithmic configurations between different algorithm implementations. MOODY offers a means to infuse semantic annotations into the configurations found by automatic tools, enabling efficient querying of the results and seamless integration across diverse sources through their incorporation into a knowledge graph. We validate our proposal by presenting four case studies.

Feature selection in small-sample high-dimensional datasets enhances classification accuracy and reduces computational time for model training. This paper introduces the filter Classification Error Impurity (CEI) as a frequency-based ranker that improves upon existing methods by better identifying non-linear patterns. According to this, the top features identified by the ensemble of CEI, along with Mutual Information (MI) and Fisher Ratio (FR), form the feature space utilized by the Adaptive Genetic Algorithm with External Repository (AGAwER) to identify the optimal feature combination in a wrapper approach. AGAwER leverages an external repository, incorporating the best solutions to enrich the Genetic Algorithm’s (GA) population, thus promoting diversity and enhancing exploration. As a result, a hybrid method called CMF-AGAwER is proposed, which surpasses existing modern feature selection methods. The implementation and data are accessible on GitHub at https://github.com/KhaosResearch/CMF-AGAwER.

Earth Observation (EO) based on Remote Sensing (RS) is gaining importance nowadays, since it offers a well-grounded technological framework for the development of advanced applications in multiple domains, such as climate change, precision agriculture, smart urbanism, safety, and many others. This promotes the continuous generation of data-driven software facilities oriented to advanced processing, analysis and visualization, which often offer enhanced computing capabilities. Nevertheless, the development of knowledge-driven approaches is still an open challenge in remote sensing, besides they provide human experts with domain knowledge representation, support for data standardization and semantic integration of sources, which indeed enhance the construction of advanced on-top applications. To this end, the use of ontologies and web semantic technologies have shown high success in knowledge representation in many fields, in which the Earth Observation is not an exception. However, as argued by the research community, there is large room for improvement in the specific case of remote sensing, where ontologies that consider the special nature and structure of different satellital and airborne data products are demanded. This article addresses, in first instance, part of this need by proposing a semantic model for the consolidation, integration, reasoning and linking of data (and meta-data), in the context of satellital remote sensing products for EO. With this objective, an OWL ontology has been developed and an RDF repository has been generated to allow advanced SPARQL querying. Although the proposal has been designed to consider remote sensing data products in general, the current study is mainly focused on the Sentinel 2 satellite mission from the Copernicus Programme of the European Space Agency (ESA). Four different use cases are showcased to check potentials of the proposed semantic model in terms of ontology integration, federated querying, data analysis and reasoning.

Modern applications of Big Data are transcending from being scalable solutions of data processing and analysis, to now provide advanced functionalities with the ability to exploit and understand the underpinning knowledge. This change is promoting the development of tools in the intersection of data processing, data analysis, knowledge extraction and management. In this paper, we propose TITAN, a software platform for managing all the life cycle of science workflows from deployment to execution in the context of Big Data applications. This platform is characterised by a design and operation mode driven by semantics at different levels: data sources, problem domain and workflow components. The proposed platform is developed upon an ontological framework of meta-data consistently managing processes and models and taking advantage of domain knowledge. TITAN comprises a well-grounded stack of Big Data technologies including Apache Kafka for inter-component communication, Apache Avro for data serialisation and Apache Spark for data analytics. A series of use cases are conducted for validation, which comprises workflow composition and semantic meta-data management in academic and real-world fields of human activity recognition and land use monitoring from satellite images.

MOTIVATION: Multiple sequence alignment (MSA) consists of finding the optimal alignment of three or more biological sequences to identify highly conserved regions that may be the result of similarities and relationships between the sequences. MSA is an optimization problem with NP-hard complexity (non-deterministic polynomial-time hardness), because the time needed to find optimal alignments raises exponentially along with the number of sequences and their length. Furthermore, the problem becomes multiobjective when more than one score is considered to assess the quality of an alignment, such as maximizing the percentage of totally conserved columns and minimizing the number of gaps. Our motivation is to provide a Python tool for solving MSA problems using evolutionary algorithms, a nonexact stochastic optimization approach that has proven to be effective to solve multiobjective problems. RESULTS: The software tool we have developed, called Sequoya, is written in the Python programming language, which offers a broad set of libraries for data analysis, visualization and parallelism. Thus, Sequoya offers a graphical tool to visualize the progress of the optimization in real time, the ability to guide the search toward a preferred region in run-time, parallel support to distribute the computation among nodes in a distributed computing system, and a graphical component to assist in the analysis of the solutions found at the end of the optimization. AVAILABILITY AND IMPLEMENTATION: Sequoya can be freely obtained from the Python Package Index (pip) or, alternatively, it can be downloaded from Github at https://github.com/benhid/Sequoya. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The analysis of gene expression levels is used in many clinical studies to know how patients evolve or to find new genetic biomarkers that could help in clinical decision making. However, the techniques and software available for these analyses are not intended for physicians, but for geneticists. However, enabling physicians to make initial discoveries on these data would benefit in the clinical assay development. RESULTS: Melanoma is a highly immunogenic tumor. Therefore, in recent years physicians have incorporated immune system altering drugs into their therapeutic arsenal against this disease, revolutionizing the treatment of patients with an advanced stage of the cancer. This has led us to explore and deepen our knowledge of the immunology surrounding melanoma, in order to optimize the approach. Within this project we have developed a database for collecting relevant clinical information for melanoma patients, including the storage of patient gene expression levels obtained from the NanoString platform (several samples are taken from each patient). The Immune Profiling Panel is used in this case. This database is being exploited through the analysis of the different expression profiles of the patients. This analysis is being done with Python, and a parallel version of the algorithms is available with Apache Spark to provide scalability as needed. CONCLUSIONS: VIGLA-M, the visual analysis tool for gene expression levels in melanoma patients is available at http://khaos.uma.es/melanoma/ . The platform with real clinical data can be accessed with a demo user account, physician, using password physician_test_7634 (if you encounter any problems, contact us at this email address: mailto: [email protected]). The initial results of the analysis of gene expression levels using these tools are providing first insights into the patients' evolution. These results are promising, but larger scale tests must be developed once new patients have been sequenced, to discover new genetic biomarkers.

Summary Big data technologies enable people to store, analyze, and utilize large amounts of complex data effectively. In this context, NoSQL (Not only Structured Query Language) databases have emerged as the most commonly used infrastructure for handling big Resource Description Framework data. In this paper, we present our proposal to materialize classified OWL (Web Ontology Language) ontologies in a NoSQL database. This materialization process enables the distributed storage of Resource Description Framework data by exploiting the inherent distribution of the NoSQL database nodes. Furthermore, this approach supposes the first step toward a novel approach to scalable OWL reasoning, which enables scalable Linked Data reasoning. To validate our proposal, we developed a prototype for a tool that stores an OWL ontology in a Cassandra database following our approach. Finally, we introduce our reasoning strategy to compute the ontology closure using this Cassandra database.

MOTIVATION: Multiple sequence alignment (MSA) is an NP-complete optimization problem found in computational biology, where the time complexity of finding an optimal alignment raises exponentially along with the number of sequences and their lengths. Additionally, to assess the quality of a MSA, a number of objectives can be taken into account, such as maximizing the sum-of-pairs, maximizing the totally conserved columns, minimizing the number of gaps, or maximizing structural information based scores such as STRIKE. An approach to deal with MSA problems is to use multi-objective metaheuristics, which are non-exact stochastic optimization methods that can produce high quality solutions to complex problems having two or more objectives to be optimized at the same time. Our motivation is to provide a multi-objective metaheuristic for MSA that can run in parallel taking advantage of multi-core-based computers. RESULTS: The software tool we propose, called M2Align (Multi-objective Multiple Sequence Alignment), is a parallel and more efficient version of the three-objective optimizer for sequence alignments MO-SAStrE, able of reducing the algorithm computing time by exploiting the computing capabilities of common multi-core CPU clusters. Our performance evaluation over datasets of the benchmark BAliBASE (v3.0) shows that significant time reductions can be achieved by using up to 20 cores. Even in sequential executions, M2Align is faster than MO-SAStrE, thanks to the encoding method used for the alignments. AVAILABILITY AND IMPLEMENTATION: M2Align is an open source project hosted in GitHub, where the source code and sample datasets can be freely obtained: https://github.com/KhaosResearch/M2Align. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multiple sequence alignment (MSA) plays a core role in most bioinformatics studies and provides a framework for the analysis of evolution in biological systems. The MSA problem consists in finding an optimal alignment of three or more sequences of nucleotides or amino acids. Different scores have been defined to assess the quality of MSA solutions, so the problem can be formulated as a multiobjective optimization problem. The number of proposals focused on this approach in the literature is scarce, and most of the works take as base algorithm the NSGA-II metaheuristic. So, there is a lack of a study involving a set of representative multiobjective metaheuristics to deal with this complex problem. Our main goal in this paper is to carry out such study. We propose a biobjective formulation for the MSA and perform an exhaustive comparative study of six multiobjective algorithms. We have considered a number of problems taken from the benchmark BAliBASE (v3.0). Our experiments reveal that the classic NSGA-II algorithm and MOCell, a cellular metaheuristic, provide the best overall performance.

MOTIVATION: Molecular docking is a method for structure-based drug design and structural molecular biology, which attempts to predict the position and orientation of a small molecule (ligand) in relation to a protein (receptor) to produce a stable complex with a minimum binding energy. One of the most widely used software packages for this purpose is AutoDock, which incorporates three metaheuristic techniques. We propose the integration of AutoDock with jMetalCpp, an optimization framework, thereby providing both single- and multi-objective algorithms that can be used to effectively solve docking problems. RESULTS: The resulting combination of AutoDock + jMetalCpp allows users of the former to easily use the metaheuristics provided by the latter. In this way, biologists have at their disposal a richer set of optimization techniques than those already provided in AutoDock. Moreover, designers of metaheuristic techniques can use molecular docking for case studies, which can lead to more efficient algorithms oriented to solving the target problems. AVAILABILITY AND IMPLEMENTATION: jMetalCpp software adapted to AutoDock is freely available as a C++ source code at http://khaos.uma.es/AutodockjMetal/.

BACKGROUND: Computing semantic relatedness between textual labels representing biological and medical concepts is a crucial task in many automated knowledge extraction and processing applications relevant to the biomedical domain, specifically due to the huge amount of new findings being published each year. Most methods benefit from making use of highly specific resources, thus reducing their usability in many real world scenarios that differ from the original assumptions. In this paper we present a simple resource-efficient method for calculating semantic relatedness in a knowledge-poor environment. The method obtains results comparable to state-of-the-art methods, while being more generic and flexible. The solution being presented here was designed to use only a relatively generic and small document corpus and its statistics, without referring to a previously defined knowledge base, thus it does not assume a 'closed' problem. RESULTS: We propose a method in which computation for two input texts is based on the idea of comparing the vocabulary associated with the best-fit documents related to those texts. As keyterm extraction is a costly process, it is done in a preprocessing step on a 'per-document' basis in order to limit the on-line processing. The actual computations are executed in a compact vector space, limited by the most informative extraction results. The method has been evaluated on five direct benchmarks by calculating correlation coefficients w.r.t. average human answers. It also has been used on Gene - Disease and Disease- Disease data pairs to highlight its potential use as a data analysis tool. Apart from comparisons with reported results, some interesting features of the method have been studied, i.e. the relationship between result quality, efficiency and applicable trimming threshold for size reduction. Experimental evaluation shows that the presented method obtains results that are comparable with current state of the art methods, even surpassing them on a majority of the benchmarks. Additionally, a possible usage scenario for the method is showcased with a real-world data experiment. CONCLUSIONS: Our method improves flexibility of the existing methods without a notable loss of quality. It is a legitimate alternative to the costly construction of specialized knowledge-rich resources.

MOTIVATION: Life Sciences have emerged as a key domain in the Linked Data community because of the diversity of data semantics and formats available through a great variety of databases and web technologies. Thus, it has been used as the perfect domain for applications in the web of data. Unfortunately, bioinformaticians are not exploiting the full potential of this already available technology, and experts in Life Sciences have real problems to discover, understand and devise how to take advantage of these interlinked (integrated) data. RESULTS: In this article, we present Bioqueries, a wiki-based portal that is aimed at community building around biological Linked Data. This tool has been designed to aid bioinformaticians in developing SPARQL queries to access biological databases exposed as Linked Data, and also to help biologists gain a deeper insight into the potential use of this technology. This public space offers several services and a collaborative infrastructure to stimulate the consumption of biological Linked Data and, therefore, contribute to implementing the benefits of the web of data in this domain. Bioqueries currently contains 215 query entries grouped by database and theme, 230 registered users and 44 end points that contain biological Resource Description Framework information. AVAILABILITY: The Bioqueries portal is freely accessible at http://bioqueries.uma.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Background Saccharomyces cerevisiae is recognized as a model system representing a simple eukaryote whose genome can be easily manipulated. Information solicited by scientists on its biological entities (Proteins, Genes, RNAs...) is scattered within several data sources like SGD, Yeastract, CYGD-MIPS, BioGrid, PhosphoGrid, etc. Because of the heterogeneity of these sources, querying them separately and then manually combining the returned results is a complex and time-consuming task for biologists most of whom are not bioinformatics expert. It also reduces and limits the use that can be made on the available data. Results To provide transparent and simultaneous access to yeast sources, we have developed YeastMed: an XML and mediator-based system. In this paper, we present our approach in developing this system which takes advantage of SB-KOM to perform the query transformation needed and a set of Data Services to reach the integrated data sources. The system is composed of a set of modules that depend heavily on XML and Semantic Web technologies. User queries are expressed in terms of a domain ontology through a simple form-based web interface. Conclusions YeastMed is the first mediation-based system specific for integrating yeast data sources. It was conceived mainly to help biologists to find simultaneously relevant data from multiple data sources. It has a biologist-friendly interface easy to use. The system is available at http://www.khaos.uma.es/yeastmed/ .

High-throughput experiments have produced large amounts of heterogeneous data in the life sciences. These data are usually represented in different formats (and sometimes in technical documents) on the Web. Inevitably, life science researchers have to deal with all these data and different formats to perform their daily research, but it is simply not possible for a single human mind to analyse all these data. The integration of data in the life sciences is a key component in the analysis of biological processes. These data may contain errors, but the curation of the vast amount of data generated in the 'omic' era cannot be done by individual researchers. To address this problem, community-driven tools could be used to assist with data analysis. In this article, we focus on a tool with social networking capabilities built on top of the SBMM (Systems Biology Metabolic Modelling) Assistant to enable the collaborative improvement of metabolic pathway models (the application is freely available at http://sbmm.uma.es/SPA).

In this work, we present a proposal for characterizing the OWL ontologies available on the Web from a significant sample. We have conducted a study to review the specific characteristics of these ontologies paying attention to features which can be important from the point of view of the ontology alignment: language, sizes, number, and kind of entities that are represented in them. As a result, we offer some statistical data that can be helpful in order to understand the current situation of OWL ontologies in the Web and, therefore to guide the process of taking decisions when developing applications for aligning them.

SUMMARY: We present Systems Biology Metabolic Modeling Assistant (SBMM Assistant), a tool built using an ontology-based mediator, and designed to facilitate metabolic modeling through the integration of data from repositories that contain valuable metabolic information. This software can be used for the visualization, design and management of metabolic networks; selection, integration and storage of metabolic information; and as an assistant for kinetic modeling. AVAILABILITY: SBMM Assistant for academic use is freely available at http://www.sbmm.uma.es.

BACKGROUND: The analysis of information in the biological domain is usually focused on the analysis of data from single on-line data sources. Unfortunately, studying a biological process requires having access to disperse, heterogeneous, autonomous data sources. In this context, an analysis of the information is not possible without the integration of such data. METHODS: KA-SB is a querying and analysis system for final users based on combining a data integration solution with a reasoner. Thus, the tool has been created with a process divided into two steps: 1) KOMF, the Khaos Ontology-based Mediator Framework, is used to retrieve information from heterogeneous and distributed databases; 2) the integrated information is crystallized in a (persistent and high performance) reasoner (DBOWL). This information could be further analyzed later (by means of querying and reasoning). RESULTS: In this paper we present a novel system that combines the use of a mediation system with the reasoning capabilities of a large scale reasoner to provide a way of finding new knowledge and of analyzing the integrated information from different databases, which is retrieved as a set of ontology instances. This tool uses a graphical query interface to build user queries easily, which shows a graphical representation of the ontology and allows users o build queries by clicking on the ontology concepts. CONCLUSION: These kinds of systems (based on KOMF) will provide users with very large amounts of information (interpreted as ontology instances once retrieved), which cannot be managed using traditional main memory-based reasoners. We propose a process for creating persistent and scalable knowledgebases from sets of OWL instances obtained by integrating heterogeneous data sources with KOMF. This process has been applied to develop a demo tool http://khaos.uma.es/KA-SB, which uses the BioPax Level 3 ontology as the integration schema, and integrates UNIPROT, KEGG, CHEBI, BRENDA and SABIORK databases.

BACKGROUND: Protein-protein interactions can be considered the basic skeleton for living organism self-organization and homeostasis. Impressive quantities of experimental data are being obtained and computational tools are essential to integrate and to organize this information. This paper presents Protopia, a biological tool that offers a way of searching for proteins and their interactions in different Protein Interaction Web Databases, as a part of a multidisciplinary initiative of our institution for the integration of biological data http://asp.uma.es. RESULTS: The tool accesses the different Databases (at present, the free version of Transfac, DIP, Hprd, Int-Act and iHop), and results are expressed with biological protein names or databases codes and can be depicted as a vector or a matrix. They can be represented and handled interactively as an organic graph. Comparison among databases is carried out using the Uniprot codes annotated for each protein. CONCLUSION: The tool locates and integrates the current information stored in the aforementioned databases, and redundancies among them are detected. Results are compatible with the most important network analysers, so that they can be compared and analysed by other world-wide known tools and platforms. The visualization possibilities help to attain this goal and they are especially interesting for handling multiple-step or complex networks.

This work is about an experiment in which we have compared the textual rendering of ontologies in order to get more accurate alignments between them. The experiments we have performed consist on three main steps: rendering in a textual way two ontologies, comparing the obtained text with several algorithms for text comparing and, using the obtained result as a factor to improve the alignments between them. As result, we got some evidences that this technique gives us a good measure of the similarity of ontologies and, therefore can allow us to improve the effectiveness of the alignment process.

With the increasing development of real applications using Semantic Web Technologies, it is necessary to provide scalable and efficient ontology querying and reasoning systems. In this paper we present DBOWL, a persistent and scalable OWL reasoner. Ontologies are stored in a relational database, using a description logic reasoner to pre-compute the class and property hierarchies and to obtain all the ontology information (i.e. properties domain and range) which is also stored in the database. Furthermore, a simple but expressive query language has been implemented, which allows us to query and reason on these ontologies. In order to show the use of the tool, we present an example that generates a relational database to store the Univ-Bench ontology. Finally, in order to asses the performance of our tool, we use LUMB, a well known benchmark to compare repositories in the Semantic Web extending it with more complex queries, and we compare the results obtained with Minerva. The results show the efficiency and scalability of DBOWL and the power of the query language.

BACKGROUND: Amines are biogenic amino acid derivatives, which play pleiotropic and very important yet complex roles in animal physiology. For many other relevant biomolecules, biochemical and molecular data are being accumulated, which need to be integrated in order to be effective in the advance of biological knowledge in the field. For this purpose, a multidisciplinary group has started an ontology-based system named the Amine System Project (ASP) for which amine-related information is the validation bench. RESULTS: In this paper, we describe the Ontology-Based Mediator developed in the Amine System Project (http://asp.uma.es) using the infrastructure of Semantic Directories, and how this system has been used to solve a case related to amine metabolism-related protein structures. CONCLUSIONS: This infrastructure is used to publish and manage not only ontologies and their relationships, but also metadata relating to the resources committed with the ontologies. The system developed is available at http://asp.uma.es/WebMediator.

MOTIVATION: In addition to existing bioinformatics software, a lot of new tools are being developed world wide to supply services for an ever growing, widely dispersed and heterogeneous collection of biological data. The integration of these resources under a common platform is a challenging task. To this end, several groups are developing integration technologies, in which services are usually registered in some sort of catalogue to allow novel discovering and accessing mechanisms to be implemented. However, each service demands specific interfaces to accommodate their parameters and it is a complicated task linking the different service inputs and outputs to solve a biological problem. RESULTS: In this work we address the design and implementation of a versatile web client to access BioMOBY compatible services (a system by which a client can interact with multiple sources of biological data regardless of the underlying format or schema) using the service description stored in the BioMOBY catalogue. The automatic interface generator significantly reduces developing time and produces uniform service access mechanisms. The design and proof of concept (for such a client) including the generic interface generator have been developed and implemented in the National Institute for Bioinformatics in Spain. AVAILABILITY: The INB (National Institute for Bioinformatics, Spain) platform is available at www.inab.org/MOWServ

Abstract In this work we present an architecture for XML‐based mediator systems and a framework for helping systems developers in the construction of mediator‐services for the integration of heterogeneous data sources. A unique feature of our architecture is its capability to manage (proprietary) user's software tools and algorithms, modelled as Extended Value Added Services (EVASs), and integrated in the data flow. The mediator offers a view of the system as a single data source where EVASs are readily available for enhancing query processing. A Web‐based graphic interface has been developed to allow dynamic and flexible EVASs inter‐connection, thus creating complex distributed bioinformatics machines. The feasibility and usefulness of our ideas has been validated by the development of a mediator system (Bio‐Broker) and by a diverse set of applications aimed at combining gene expression data with genomic, sequence‐based and structural information, so as to provide a general, transparent and powerful solution that integrates data analysis tools and algorithms. Copyright © 2006 John Wiley & Sons, Ltd.

A dataflow model to obtain parallelism in the evaluation of Datalog is presented. This model performs query evaluation as a dataflow through a network of communicating concurrent processes capable of solving the query. This process network is based on the intensional database definition, plus the concrete query to be evaluated. A cost model to cope with the load balancing problem is described. A load balancing algorithm is presented and discussed. An algorithm to optimize the evaluation is described which is based on process network rewriting. This utilizes information in the query bindings to be evaluated in order to optimize the dataflow graph.