Resolución SL*: Un paradigma basado en resolución lineal para la demostración automática

El trabajo incluido en la presente tesis se enmarca dentro del campo de la demostración automática de teoremas y consiste en la estudio, definición y desarrollo de un paradigma de resolución lineal, denominado Resolución SL*. La razón para utilizar la denominación de paradigma reside en el hecho de que en sí misma resolución SL* no es un procedimiento, sino que se puede entender como una forma de razonamiento con ciertos parámetros cuya instanciación da lugar a diferentes procedimientos que son adecuados para el tratamiento de distintos tipos de problemas. Por otro lado, se le ha dado el nombre de resolución SL* porque, como posteriormente se explicará, está muy cercano a Eliminación de Modelos y a resolución SL (de ahí la primera parte del nombre). El asterisco final quiere denotar su parametrización, de forma que los procedimientos instancias de resolución SL* serán denominados con una letra más en vez del asterisco, como posteriormente se verá. La tesis ha sido dividida en cuatro capítulos que se describen brevemente a continuación. En el primero se realiza una breve introducción histórica a la demostración automática, que va desde los orígenes de la lógica con el uso de las primeras notaciones matemáticas formales en el siglo XVI hasta la aparición de los resultados más importantes de la lógica descubiertos por Herbrand, Gödel, Church, etc. Se hace un especial hincapié en este capítulo en la demostración automática realizando un recorrido desde sus orígenes a finales del siglo XVIII hasta el momento actual, en el cual es posible ver cuál ha sido la evolución de este campo y qué descubrimientos y resultados se pueden presentar como los principales puntos de inflexión. En el segundo capítulo se presentan la resolución lineal y algunos de sus principales refinamientos, ya que resolución SL* es un variación de resolución SL y por tanto de resolución lineal. Para ello se introduce el principio de resolución, viendo los problemas de su mecanización, y posteriormente se ven dos refinamientos de resolución: resolución semántica y resolución lineal. Para concluir se estudian los principales refinamientos de resolución lineal: resolución de entrada, resolución lineal con fusión, resolución lineal con subsumción, resolución lineal ordenada, resolución MTOSS y TOSS, Eliminación de Modelos, resolución SL y el sistema MESON. En el tercer capítulo se presentan y estudian con profundidad las principales aportaciones al campo de la demostración automática que se han producido en los últimos años y que están cercanas a la aproximación del presente trabajo. Se han incluido los siguientes trabajos: el demostrador PTTP de Stickel, el sistema MESON basado en secuencias de Plaisted, el demostrador SATCHMO de Manthey y Bry, los procedimientos Near-Horn Prolog de Loveland y otros autores y, por último, el demostrador SETHEO de Bibel y otros autores. Obviamente no se han incluido todos los demostradores y procedimientos, pero sí aquellos que se han considerado como los más interesantes y cercanos a resolución SL* de manera que sea posible realizar comparaciones, de forma que queden patentes las aportaciones realizadas. En el cuarto capítulo se presenta resolución SL*. Se da la definición formal de la misma y se introduce el concepto fundamental de elección de ancestros. La elección de ancestros es el mecanismo que permite controlar la aplicación de la resolución de ancestro haciendo posible una reducción del coste de su aplicación y una adecuación de resolución SL* al tipo de problema a tratar. Posteriormente se ven las principales instancias de resolución SL*, los procedimientos SLT y SLP. En este capítulo se hace un especial hincapié en la elección de ancestros, ya que es la principal aportación de resolución SL*, analizando tanto las ventajas que aporta asociadas al incremento de la eficiencia como el hecho de dotar a resolución SL* la capacidad de adaptarse a los problemas que trata. También en este capítulo se presenta una implementación de resolución SL*, en particular del procedimiento SLT, y se incluyen resultados sobre un conjunto extenso de problemas del campo de la demostración automática. En la última sección de este capítulo se realiza una comparación de resolución SL* con los demostradores y sistemas más cercanos, tanto a nivel de características como de resultados.Casamayor Rodenas, JC. (1996). Resolución SL*: Un paradigma basado en resolución lineal para la demostración automática [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/6023Palanci

Towards the Understanding of the Human Genome: A Holistic Conceptual Modeling Approach

[EN] Understanding the human genome is a great scientific challenge, whose achievement requires effective data manipulation mechanisms. The non-stop evolution of both new knowledge and more efficient sequencing technologies generates a kind of genome data chaos. This chaos complicates the use of computational resources that obtain data and align them into specific actions. Conceptual model-based techniques should play a fundamental role in turning data into actionable knowledge. However, current solutions do not give a crucial role in the task of modeling that it should have to obtain a precise understanding of this domain. Hundreds of different data sources exist, but they have heterogeneous, imprecise, and inconsistent data. It is remarkably hard to have a unified data perspective that covers the genomic data from genome to transcriptome and proteome, which could facilitate semantic data integration. This paper focuses on how to design a conceptual model of the human genome that could be used as the key artifact to share, integrate, and understand the various types of datasets used in the genomic domain. We provide a full conceptual picture of relevant data in genomics and how semantic data integration is much more effective by conceptually integrating the diverse types of existing data. We show how such a conceptual model has been built, focusing on the conceptual problems that were solved to adequately model concepts whose knowledge is under constant evolution. We show how the use of the initial versions of the conceptual model in practice has allowed us to identify new features to incorporate in the model, achieving a continuous improvement process. The current version is ready to be used as the key artifact in projects where conceptually combining multiple levels of data helps to provide valuable insights that would be hard to obtain without it.This work was supported in part by the Spanish State Research Agency, in part by the Generalitat Valenciana under Grant TIN2016-80811-P and Grant PROMETEO/2018/176, and in part by European Regional Development Fund (ERDF).García-Simón, A.; León-Palacio, A.; Reyes Román, JF.; Casamayor Rodenas, JC.; Pastor López, O. (2020). Towards the Understanding of the Human Genome: A Holistic Conceptual Modeling Approach. IEEE Access. 8:197111-197123. https://doi.org/10.1109/ACCESS.2020.3034793S197111197123

Using conceptual modeling to improve genome data management

[EN] With advances in genomic sequencing technology, a large amount of data is publicly available for the research community to extract meaningful and reliable associations among risk genes and the mechanisms of disease. However, this exponential growth of data is spread in over thousand heterogeneous repositories, represented in multiple formats and with different levels of quality what hinders the differentiation of clinically valid relationships from those that are less well-sustained and that could lead to wrong diagnosis. This paper presents how conceptual models can play a key role to efficiently manage genomic data. These data must be accessible, informative and reliable enough to extract valuable knowledge in the context of the identification of evidence supporting the relationship between DNA variants and disease. The approach presented in this paper provides a solution that help researchers to organize, store and process information focusing only on the data that are relevant and minimizing the impact that the information overload has in clinical and research contexts. A case-study (epilepsy) is also presented, to demonstrate its application in a real context.Spanish State Research Agency and the Generalitat Valenciana under the projects TIN2016-80811-P and PROMETEO/2018/176; ERDF.Pastor López, O.; León-Palacio, A.; Reyes Román, JF.; García-Simón, A.; Casamayor Rodenas, JC. (2020). Using conceptual modeling to improve genome data management. Briefings in Bioinformatics. 22(1):45-54. https://doi.org/10.1093/bib/bbaa100S4554221McCombie, W. R., McPherson, J. D., & Mardis, E. R. (2018). Next-Generation Sequencing Technologies. Cold Spring Harbor Perspectives in Medicine, 9(11), a036798. doi:10.1101/cshperspect.a036798Condit, C. M., Achter, P. J., Lauer, I., & Sefcovic, E. (2001). The changing meanings of ?mutation:? A contextualized study of public discourse. Human Mutation, 19(1), 69-75. doi:10.1002/humu.10023Karki, R., Pandya, D., Elston, R. C., & Ferlini, C. (2015). Defining «mutation» and «polymorphism» in the era of personal genomics. BMC Medical Genomics, 8(1). doi:10.1186/s12920-015-0115-zHamid, J. S., Hu, P., Roslin, N. M., Ling, V., Greenwood, C. M. T., & Beyene, J. (2009). Data Integration in Genetics and Genomics: Methods and Challenges. Human Genomics and Proteomics, 1(1). doi:10.4061/2009/869093Baudhuin, L. M., Biesecker, L. G., Burke, W., Green, E. D., & Green, R. C. (2019). Predictive and Precision Medicine with Genomic Data. Clinical Chemistry, 66(1), 33-41. doi:10.1373/clinchem.2019.304345Amaral, G., & Guizzardi, G. (2019). On the Application of Ontological Patterns for Conceptual Modeling in Multidimensional Models. Lecture Notes in Computer Science, 215-231. doi:10.1007/978-3-030-28730-6_14Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., … Sherlock, G. (2000). Gene Ontology: tool for the unification of biology. Nature Genetics, 25(1), 25-29. doi:10.1038/75556Eilbeck, K., Lewis, S. E., Mungall, C. J., Yandell, M., Stein, L., Durbin, R., & Ashburner, M. (2005). Genome Biology, 6(5), R44. doi:10.1186/gb-2005-6-5-r44Vihinen, M. (2013). Variation Ontology for annotation of variation effects and mechanisms. Genome Research, 24(2), 356-364. doi:10.1101/gr.157495.113Köhler, S., Carmody, L., Vasilevsky, N., Jacobsen, J. O. B., Danis, D., Gourdine, J.-P., … McMurry, J. A. (2018). Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Research, 47(D1), D1018-D1027. doi:10.1093/nar/gky1105Proceedings of the Eleventh International Conference on Data Engineering. (1995). Proceedings of the Eleventh International Conference on Data Engineering. doi:10.1109/icde.1995.380416Okayama, T., Tamura, T., Gojobori, T., Tateno, Y., Ikeo, K., Miyazaki, S., … Sugawara, H. (1998). Formal design and implementation of an improved DDBJ DNA database with a new schema and object-oriented library. Bioinformatics, 14(6), 472-478. doi:10.1093/bioinformatics/14.6.472Medigue, C., Rechenmann, F., Danchin, A., & Viari, A. (1999). Imagene: an integrated computer environment for sequence annotation and analysis. Bioinformatics, 15(1), 2-15. doi:10.1093/bioinformatics/15.1.2Paton, N. W., Khan, S. A., Hayes, A., Moussouni, F., Brass, A., Eilbeck, K., … Oliver, S. G. (2000). Conceptual modelling of genomic information. Bioinformatics, 16(6), 548-557. doi:10.1093/bioinformatics/16.6.548Vihinen, M., Hancock, J. M., Maglott, D. R., Landrum, M. J., Schaafsma, G. C. P., & Taschner, P. (2016). Human Variome Project Quality Assessment Criteria for Variation Databases. Human Mutation, 37(6), 549-558. doi:10.1002/humu.22976Fleuren, W. W. M., & Alkema, W. (2015). Application of text mining in the biomedical domain. Methods, 74, 97-106. doi:10.1016/j.ymeth.2015.01.015Salzberg, S. L. (2007). Genome re-annotation: a wiki solution? Genome Biology, 8(1). doi:10.1186/gb-2007-8-1-102Rigden, D. J., & Fernández, X. M. (2018). The 26th annual Nucleic Acids Research database issue and Molecular Biology Database Collection. Nucleic Acids Research, 47(D1), D1-D7. doi:10.1093/nar/gky1267Reyes Román, J. F., García, A., Rueda, U., & Pastor, Ó. (2019). GenesLove.Me 2.0: Improving the Prioritization of Genetic Variations. Evaluation of Novel Approaches to Software Engineering, 314-333. doi:10.1007/978-3-030-22559-9_14Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., … Rehm, H. L. (2015). Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in Medicine, 17(5), 405-423. doi:10.1038/gim.2015.30Kelly, M. A., Caleshu, C., Morales, A., Buchan, J., Wolf, Z., … Funke, B. (2018). Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen’s Inherited Cardiomyopathy Expert Panel. Genetics in Medicine, 20(3), 351-359. doi:10.1038/gim.2017.21

Design and Development of an Information System to Manage Clinical Data about Usher Syndrome Based on Conceptual Modeling

[EN] The inefficient management of clinical data in many research environments is a problem which slows down the service provided to patients. The benefits of an Information System created following the conceptual modeling rules have been proved in multiple environments with data management difficulties. The main hurdle to overcome is the large gap between the language and concepts employed by informaticians and the ones used by biologists. The work described in this paper shows how these technologies can also be applied to the clinical domain, after a long period of mutual approaching in order to understand each other. The research clinical data of an expert research group on Usher syndrome have been studied, analyzed and redesigned using conceptual modeling, helping this group to offer a better service.It is important to highlight that this work has been done under the framework of the Cátedra Tecnologías para la Salud of the Universitat Politècnica de València financed by INDRA Systems.Burriel Coll, V.; Pastor Cubillo, MÁ.; Celma Giménez, M.; Casamayor Rodenas, JC.; Mota Herranz, L. (2013). Design and Development of an Information System to Manage Clinical Data about Usher Syndrome Based on Conceptual Modeling. IARIA XPS Press. http://hdl.handle.net/10251/75237

On how to generalize specie-specific conceptual schemes to generate a species-independent Conceptual Schema of the Genome

[EN] Background Understanding the genome, with all of its components and intrinsic relationships, is a great challenge. Conceptual modeling techniques have been used as a means to face this challenge. The heterogeneity and idiosyncrasy of genomic use cases mean that conceptual modeling techniques are used to generate conceptual schemes that focus on too specific scenarios (i.e., they are species-specific conceptual schemes). Our research group developed two different conceptual schemes. The first one is the Conceptual Schema of the Human Genome, which is intended to improve Precision Medicine and genetic diagnosis. The second one is the Conceptual Schema of the Citrus Genome, which is intended to identify the genetic cause of relevant phenotypes in the agri-food field. Methods Our two conceptual schemes have been ontologically compared to identify their similarities and differences. Based on this comparison, several changes have been performed in the Conceptual Schema of the Human Genome in order to obtain the first version of a species-independent Conceptual Schema of the Genome. Identifying the different genome information items used in each genomic case study has been essential in achieving our goal. The changes needed to provide an expanded, more generic version of the Conceptual Schema of the Human Genome are analyzed and discussed. Results This work presents a new CS called the Conceptual Schema of the Genome that is ready to be adapted to any specific working genome-based context (i.e., species-independent). Conclusion The generated Conceptual Schema of the Genome works as a global, generic element from which conceptual views can be created in order to work with any specific species. This first working version can be used in the human use case, in the citrus use case, and, potentially, in more use cases of other species.This work was supported by the Spanish Ministry of Science and Innovation through Project DataME (ref: TIN2016-80811-P) and the Generalitat Valenciana through project GISPRO (PROMETEO/2018/176).García-Simón, A.; Casamayor Rodenas, JC. (2021). On how to generalize specie-specific conceptual schemes to generate a species-independent Conceptual Schema of the Genome. BMC Bioinformatics. 22(13):1-26. https://doi.org/10.1186/s12859-021-04237-xS126221

A Conceptual Model-Based Approach to Improve the Representation and Management of Omics Data in Precision Medicine

[EN] Precision medicine has emerged as a disrupting medical model to transform a historically reactive medicine into a proactive one that focuses on delivering individualized treatment. A relevant challenge of precision medicine is to integrate the large amount of omics data that exists. This data has a high degree of heterogeneity, dispersion, and isolation. In addition, there is a lack of a solid ontological commitment regarding domain concepts and definitions, and a unified guideline of how to transform data into knowledge is missing. In this work, we report our experience applying conceptual modeling to deal with these challenges in a specific genomics dimension, i.e., Precision Medicine. To do so, we have applied conceptual modeling techniques. The use of these techniques allows us to create representations of the world (i.e., conceptual schemes) that can be used for the purposes of understanding, communicating, and problem-solving. They also help to establish a common ontological framework to facilitate both communication and knowledge evolution in complex domains. We identify a set of limitations that emerged after working in a precision medicine context, and we describe how we have solved them using conceptual modeling. Thus, the main contribution of this work is to present the subsequent Conceptual Schema that allowed us to overcome these limitations, which provides a better representation of proteomics data and eases its integration.This work was supported in part by the Spanish State Research Agency and the Generalitat Valenciana under Project PROMETEO/2018/176, Project PDC2021-121243-I00, and Project INNEST/2021/57; and in part by the European Regional Development Fund (ERDF) and the European Union NextGenerationEU/PRTR.García-Simón, A.; León-Palacio, A.; Reyes Román, JF.; Casamayor Rodenas, JC.; Pastor López, O. (2021). A Conceptual Model-Based Approach to Improve the Representation and Management of Omics Data in Precision Medicine. IEEE Access. 9:154071-154085. https://doi.org/10.1109/ACCESS.2021.3128757S154071154085