Novel Algorithms for Cross-Ontology Multi-Level Data Mining

The wide spread use of ontologies in many scientific areas creates a wealth of ontologyannotated data and necessitates the development of ontology-based data mining algorithms. We have developed generalization and mining algorithms for discovering cross-ontology relationships via ontology-based data mining. We present new interestingness measures to evaluate the discovered cross-ontology relationships. The methods presented in this dissertation employ generalization as an ontology traversal technique for the discovery of interesting and informative relationships at multiple levels of abstraction between concepts from different ontologies. The generalization algorithms combine ontological annotations with the structure and semantics of the ontologies themselves to discover interesting crossontology relationships. The first algorithm uses the depth of ontological concepts as a guide for generalization. The ontology annotations are translated to higher levels of abstraction one level at a time accompanied by incremental association rule mining. The second algorithm conducts a generalization of ontology terms to all their ancestors via transitive ontology relations and then mines cross-ontology multi-level association rules from the generalized transactions. Our interestingness measures use implicit knowledge conveyed by the relation semantics of the ontologies to capture the usefulness of cross-ontology relationships. We describe the use of information theoretic metrics to capture the interestingness of cross-ontology relationships and the specificity of ontology terms with respect to an annotation dataset. Our generalization and data mining agorithms are applied to the Gene Ontology and the postnatal Mouse Anatomy Ontology. The results presented in this work demonstrate that our generalization algorithms and interestingness measures discover more interesting and better quality relationships than approaches that do not use generalization. Our algorithms can be used by researchers and ontology developers to discover inter-ontology connections. Additionally, the cross-ontology relationships discovered using our algorithms can be used by researchers to understand different aspects of entities that interest them

From phenotypes to trees of life : a metamodel-driven approach for the integration of taxonomy models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Several projects aim at gathering together data concerning life around the world, in order to systematize them and produce a big, unified tree of life. Rather than a static single picture of the living world, this kind of tree: (i) is a result of a dynamic interaction among several models produced by biologists for describing life and expressing how life changes and evolves as time goes by, (ii) is not unique, since there are different competing perspectives describing life (morphology, behavior, ecology, genetics etc.) and different methods of reconstructing evolutionary trees. Our work addresses these problems by proposing a 'superimposed metamodel' mechanism, which acts as a modeling skeleton, supporting a unified view and articulation of models/ontologies involved in tasks that start at collecting data from the field towards producing descriptions and evolutionary trees. It enables to externalize specific knowledge as ontologies and to trace the entire rationale from one extreme of the process to the other one. This paper shows practical experiments in which we explore such characteristics as: guiding the expression of evolutionary hypotheses from observational data, going backwards on the provenance path, or evaluating changes of the tree in front of new evidences collected in the field.Several projects aim at gathering together data concerning life around the world, in order to systematize them and produce a big, unified tree of life. Rather than a static single picture of the living world, this kind of tree: (i) is a result of a dynami16572FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)sem informaçãosem informaçãoIEEE 10th. International conference on e-scienc

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Trends in modeling Biomedical Complex Systems.

In this paper we provide an introduction to the techniques for multi-scale complex biological systems, from the single bio-molecule to the cell, combining theoretical modeling, experiments, informatics tools and technologies suitable for biological and biomedical research, which are becoming increasingly multidisciplinary, multidimensional and information-driven. The most important concepts on mathematical modeling methodologies and statistical inference, bioinformatics and standards tools to investigate complex biomedical systems are discussed and the prominent literature useful to both the practitioner and the theoretician are presented.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Trends in modeling Biomedical Complex Systems

In this paper we provide an introduction to the techniques for multi-scale complex biological systems, from the single bio-molecule to the cell, combining theoretical modeling, experiments, informatics tools and technologies suitable for biological and biomedical research, which are becoming increasingly multidisciplinary, multidimensional and information-driven. The most important concepts on mathematical modeling methodologies and statistical inference, bioinformatics and standards tools to investigate complex biomedical systems are discussed and the prominent literature useful to both the practitioner and the theoretician are presented

Gerando redes de conhecimento a partir de descrições de fenótipos

Orientadores: André Santanchè, Júlio César dos ReisDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Diversos sistemas computacionais usam informações sobre seres vivos, tais como chaves de identificação ¿ artefatos criados por biólogos para identificar espécimes de seres vivos seguindo uma cadeia de questões acerca das suas características observáveis (fenótipos). Tais questões estão em formato de texto livre, por exemplo, "Possui olhos grandes e pre- tos". Contudo, texto livre dificulta a interpretação de informação por máquinas, limitando sua capacidade de realização de tarefas de busca, integração e comparação de termos. Esta dissertação propõe um método para extrair informação a respeito de fenótipos a partir de textos escritos em linguagem natural, colocando-os no formato de Entidade-Qualidade ¿ um formato de dados biológicos para representar estruturas anatômicas (Entidade) e o seu modificador (Qualidade). A proposta permite que Entidades e Qualidades, reconhecidas automaticamente a partir de informação do nível textual, sejam relacionadas com con- ceitos presentes em ontologias de domínio. Ela adota ferramentas de Processamento de Linguagem Natural existentes, bem como contribui com novas técnicas que exploram as características de escrita e estruturação implícitas em textos presentes nas chaves de iden- tificação. A abordagem foi validada utilizando os dados da base FishBase, sobre a qual foram conduzidos experimentos explorando um conjunto de testes anotado manualmente para avaliar a precisão e aplicabilidade do método de extração proposto. Os resultados obtidos mostram os benefícios da técnica e possibilidades de estudos científicos utilizando a rede de conhecimento extraídaAbstract: Several computing systems rely on information about living beings, such as identification keys ¿ artifacts created by biologists to identify specimens following a flow of questions about their observable characters (phenotype). These questions are described in a free- text format, e.g., "big and black eye". Free-texts hamper the automatic information interpretation by machines, limiting their ability to perform search and comparison of terms, as well as integration tasks. This thesis proposes a method to extract phenotypic information from natural language texts from biology legacy information systems, trans- forming them in an Entity-Quality formalism ¿ a format to represent each phenotype character (Entity) and its state (Quality). Our approach aligns automatically recognized Entities and Qualities with domain concepts described in ontologies. It adopts existing Natural Language Processing techniques, adding an extra original step, which exploits intrinsic characteristics of phenotypic descriptions and of the organizational structure of identification keys. The approach was validated over the FishBase data. We conducted extensive experiments based on a manually annotated Gold Standard set to assess the precision and applicability of the proposed extraction method. The obtained results re- veal the feasibility of our technique, its benefits and possibilities of scientific studies using the extracted knowledge networkMestradoCiência da ComputaçãoMestre em Ciência da Computação1406900CAPE

Uberon, an integrative multi-species anatomy ontology

We present Uberon, an integrated cross-species ontology consisting of over 6,500 classes representing a variety of anatomical entities, organized according to traditional anatomical classification criteria. The ontology represents structures in a species-neutral way and includes extensive associations to existing species-centric anatomical ontologies, allowing integration of model organism and human data. Uberon provides a necessary bridge between anatomical structures in different taxa for cross-species inference. It uses novel methods for representing taxonomic variation, and has proved to be essential for translational phenotype analyses. Uberon is available at http://uberon.or