Improving approximation of domain-focused, corpus-based, lexical semantic relatedness

Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importance in many domain-specific scenarios. The problem of most state-of-the-art methods for calculating domain-specific semantic relatedness is their dependence on highly specialized, structured knowledge resources, which makes these methods poorly adaptable for many usage scenarios. On the other hand, the domain knowledge in the fields such as Life Sciences has become more and more accessible, but mostly in its unstructured form - as texts in large document collections, which makes its use more challenging for automated processing. In this dissertation, three new corpus-based methods for approximating domain-specific textual semantic relatedness are presented and evaluated with a set of standard benchmarks focused on the field of biomedicine. Nonetheless, the proposed measures are general enough to be adapted to other domain-focused scenarios. The evaluation involves comparisons with other relevant state-of-the-art measures for calculating semantic relatedness and the results suggest that the methods presented here perform comparably or better than other approaches. Additionally, the dissertation also presents an experiment, in which one of the proposed methods is applied within an ontology matching system, DisMatch. The performance of the system was evaluated externally on a biomedically themed ‘Phenotype’ track of the Ontology Alignment Evaluation Initiative 2016 campaign. The results of the track indicate, that the use distributional semantic relatedness for ontology matching is promising, as the system presented in this thesis did stand out in detecting correct mappings that were not detected by any other systems participating in the track. The work presented in the dissertation indicates an improvement achieved w.r.t. the stat-of-the-art through the domain adapted use of the distributional principle (i.e. the presented methods are corpus-based and do not require additional resources). The ontology matching experiment showcases practical implications of the presented theoretical body of work

KNIT: Ontology reusability through knowledge graph exploration

Ontologies have become a standard for knowledge representation across several domains. In Life Sciences, numerous ontologies have been introduced to represent human knowledge, often providing overlapping or conflicting perspectives. These ontologies are usually published as OWL or OBO, and are often registered in open repositories, e.g., BioPortal. However, the task of finding the concepts (classes and their properties) defined in the existing ontologies and the relationships between these concepts across different ontologies – for example, for developing a new ontology aligned with the existing ones – requires a great deal of manual effort in searching through the public repositories for candidate ontologies and their entities. In this work, we develop a new tool, KNIT, to automatically explore open repositories to help users fetch the previously designed concepts using keywords. User-specified keywords are then used to retrieve matching names of classes or properties. KNIT then creates a draft knowledge graph populated with the concepts and relationships retrieved from the existing ontologies. Furthermore, following the process of ontology learning, our tool refines this first draft of an ontology. We present three BioPortal-specific use cases for our tool. These use cases outline the development of new knowledge graphs and ontologies in the sub-domains of biology: genes and diseases, virome and drugs.This work has been funded by grant PID2020-112540RB-C4121, AETHER-UMA (A smart data holistic approach for context-aware data analytics: semantics and context exploitation). Funding for open access charge: Universidad de Málaga / CBUA

La enseñanza del metabolismo: retos y oportunidades

En el marco del Proyecto de Innovación Educativa de la Universidad de Málaga PIE15-163, cuya descripción y resultados incluimos, decidimos que esta era una excelente oportunidad para reflexionar acerca de la enseñanza del metabolismo y de poner por escrito dichas reflexiones en un libro. Quisimos y pudimos contar con la colaboración de buena parte de los compañeros del Departamento de Biología Molecular y Bioquímica que apoyaron con su firma el proyecto PIE15-163 y extendimos nuestra invitaciones a otros compañeros de dentro y fuera de la Universidad de Málaga. Del Departamento de Biología Molecular y Bioquímica de la Universidad de Málaga hemos recibido aportaciones de los catedráticos Victoriano Valpuesta Fernández, Ana Rodríguez Quesada y Antonio Heredia Bayona, los profesores titulares María Josefa Pérez Rodríguez, José Luis Urdiales Ruiz e Ignacio Fajardo Paredes y la investigadora postdoctoral y profesora sustituta interina Beatriz Martínez Poveda. De otros departamentos de la Universidad de Málaga hemos contado con las aportaciones de la catedrática del Departamento de Especialidades Quirúrgicas, Bioquímica e Inmunología Pilar Morata Losa, del catedrático del Departamento de Lenguajes y Ciencias de la Computación José Francisco Aldana Montes y los componentes de su grupo de investigación Khaos Ismael Navas Delgado, María Jesús García Godoy, Esteban López Camacho y Maciej Rybinski, del catedrático Ángel Blanco López, del Área de Conocimiento de Didáctica de las Ciencias Experimentales y del Doctor en Ciencias Químicas y actual doctorando del Programa de Doctorado "Educación y Comunicación Social" Ángel Luis García Ponce. De fuera de la Universidad de Málaga, hemos contado con las aportaciones del catedrático de la Universidad de La Laguna Néstor V. Torres Darias, de la catedrática de la Universitat de les Illes Balears Pilar Roca Salom y de sus compañeros los profesores Jorge Sastre Serra y Jordi Oliver, de los catedráticos de la Universidad de Granada Rafael Salto González y María Dolores Girón González y su colaborador el Dr. José Dámaso Vílchez Rienda, del profesor titular de la Universidad de Alcalá Ángel Herráez, del investigador postdoctoral de la Universidad de Erlangen (Alemania) Guido Santos y del investigador postdoctoral de la empresa Brain Dynamics Carlos Rodríguez Caso.Hemos estructurado los contenidos del libro en diversas secciones. La primera presenta el Proyecto en cuyo marco se ha gestado la iniciativa que ha conducido a la edición del presente libro. La segunda sección la hemos titulado "¿Qué metabolismo?" e incluye diversas aportaciones personales que reflexionan acerca de qué metabolismo debe conocer un graduado en Bioquímica, en Biología, en Química, en Farmacia o en Medicina, así como una aportación acerca de qué bioquímica estructural y enzimología son útiles y necesarias para un estudiante que vaya a afrontar el estudio del metabolismo. La tercera sección, "Bases conceptuales", analiza las aportaciones del aprendizaje colaborativo, el contrato de aprendizaje y el aprendizaje basado en la resolución de casos prácticos a la mejora del proceso enseñanza-aprendizaje dentro del campo de la Bioquímica y Biología Molecular, más concretamente en el estudio del metabolismo. La cuarta sección se titula "Herramientas", es la más extensa e incluye las diversas aportaciones centradas en propuestas concretas de aplicación relevantes y útiles para la mejora de la docencia-aprendizaje del metabolismo. Sigue una sección dedicada a presentar de forma resumida los "Resultados" del proyecto PIE15-163. El libro concluye con una "coda final" en la que se reflexiona acerca del aprendizaje de la Química a la luz de la investigación didáctica.Patrocinado por el Proyecto de Innovación Educativa de la Universidad de Málaga PIE15-16

tESA: a distributional measure for calculating semantic relatedness.

Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importance in the ever growing domain of biomedical informatics. The problem of most state-of-the-art methods for calculating semantic relatedness is their dependence on highly specialized, structured knowledge resources, which makes these methods poorly adaptable for many usage scenarios. On the other hand, the domain knowledge in the Life Sciences has become more and more accessible, but mostly in its unstructured form - as texts in large document collections, which makes its use more challenging for automated processing. In this paper we present tESA, an extension to a well known Explicit Semantic Relatedness (ESA) method. In our extension we use two separate sets of vectors, corresponding to different sections of the articles from the underlying corpus of documents, as opposed to the original method, which only uses a single vector space. We present an evaluation of Life Sciences domain-focused applicability of both tESA and domain-adapted Explicit Semantic Analysis. The methods are tested against a set of standard benchmarks established for the evaluation of biomedical semantic relatedness quality. Our experiments show that the propsed method achieves results comparable with or superior to the current state-of-the-art methods. Additionally, a comparative discussion of the results obtained with tESA and ESA is presented, together with a study of the adaptability of the methods to different corpora and their performance with different input parameters. Our findings suggest that combined use of the semantics from different sections (i.e. extending the original ESA methodology with the use of title vectors) of the documents of scientific corpora may be used to enhance the performance of a distributional semantic relatedness measures, which can be observed in the largest reference datasets. We also present the impact of the proposed extension on the size of distributional representations

DisMatch results for OAEI 2016

DisMatch is an experimental ontology matching system based on the use of corpus based distributional measure for approximating se- mantic relatedness. Through the use of a domain-related corpus, the measure can be applied to a problem focused on the domain of the cor- pus, here being the Disease and Phenotype track. In this paper, we aim to briefly present the proposed approach and the results obtained in the evaluation, as well as some early conclusions regarding the performance of DisMatch.Ministerio de Educación y Ciencia TIN2014-58304-RJunta de Andalucía P11-TIC-7529Junta de Andalucía P12-TIC-151

Análisis de los datos del acelerómetro para detección de actividades

La inactividad física es uno de los principales factores de riesgo de mortalidad y su relación con las principales enfermedades crónicas es objeto de intensas investigaciones medicas. Un método objetivo de la evaluación de la actividad de las personas es el uso de acelerómetros. En este trabajo se presenta un experimento para evaluar la viabilidad de la detección automática de algunos tipos de actividades a través de algoritmos supervisados de Deep Learning.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

VIGLA-M: visual gene expression data analytics

Abstract 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

Introducing the HOBBIT platform into the ontology alignment evaluation campaign

OM is co-located with the 17th International Semantic Web Conference (ISWC)International audienceThis paper describes the Ontology Alignment Evaluation Initiative 2017.5 pre-campaign. Like in 2012, when we transitioned the evaluation to the SEALS platform, we have also conducted a pre-campaign to assess the feasibility of moving to the HOBBIT platform. We report the experiences of this pre-campaign and discuss the future steps for the OAEI