Development of bioinformatics tools and studies in biomedical association networks for the analysis of human genetic diseases

Fecha de lectura de Tesis Doctoral: 18 de marzo 2019.El presente trabajo de tesis doctoral se centra en el análisis en red y desarrollo de herramientas bioinformáticas para la determinación de las causas que dan lugar a las enfermedades con base genética. Mediante el análisis de sistemas de red se pueden asociar fenotipos patológicos y las regiones del genoma que potencialmente sean su causa a partir de información de pacientes. Estas asociaciones fenotipo-genotipo pueden emplearse para el desarrollo de herramientas de apoyo al diagnóstico genético de pacientes con un cuadro fenotípico complejo, de manera que puedan dar información sobre las regiones del genoma que potencialmente estén afectadas en un paciente a partir de sus fenotipos patológicos observados. Del mismo modo, estas regiones asociadas a fenotipos patológicos pueden analizarse para determinar los elementos funcionales del genoma que sean la causa de la enfermedad. Este análisis incluye tanto genes como elementos reguladores, ya que se ha demostrado que un 80% de las enfermedades caracterizadas mediante análisis del genoma completo han sido asociadas a regiones no codificantes del genoma, en las cuales se encuentran los elementos reguladores. Una vez determinados los elementos funcionales existentes en las regiones del genoma asociadas a fenotipos patológicos, se pueden determinar los sistemas biológicos que estén afectados en el paciente. Sin embargo, no todos los genes tienen anotaciones funcionales que muestren a qué sistemas afectan. Esta funcionalidad viene dada por el producto génico, las proteínas, que a su vez constan de dominios que les confieren su función y/o estructura. De nuevo, mediante análisis de red se pueden asociar dominios de proteínas con anotaciones funciones a partir de información de proteínas, con el fin de poder usar esas asociaciones dominio-función para predecir la posible función desconocida de proteínas en base a sus dominios

Machine Learning Approaches for Identifying Cancer Biomarkers Using Next Generation Sequencing

Identifying biomarkers that can be used to classify certain disease stages or predict when a disease becomes more aggressive is one of the most important applications of machine learning. Next generation sequencing (NGS) is a state-of-the-art method that enables fast sequencing of DNA or RNA samples. The output usually contains a very large file that consists of base pairs of DNA or RNA. The generated data can be analyzed to provide gene expression, chromosome counting, detection of mutations on the genes, and detecting levels of copy number variations or alterations in specific genes, just as examples. NGS is leading the way to explore the human genome, enabling the future of personalized medicine. In this thesis, a demonstration is done on how machine learning is used extensively to identify genes that can be used to predict prostate cancer stages with very high accuracy, using gene expression. We have also been successful in predicting the location of prostate tumors based on gene expression. In addition, traditional biomarker identification approaches, typically, use machine learning techniques to identify a number of genes and macromolecules as biomarkers that can be used to diagnose specific diseases or states of diseases with very high accuracy, using molecular measurements such as mutations, gene expression, copy number variations, and others. However, experts\u27 opinions and knowledge is required to validate such findings. We, therefore, also introduce a new machine learning model that incorporates a knowledge-assisted system used to integrate the findings of the DisGeNET database, which is a framework that contains proven relationships among diseases and genes. The machine learning pipeline starts by reducing the number of features using a filter-based feature selection method. The DisGeNET database is used to score each gene related to the given cancer name. Then, a wrapper-based feature-selection algorithm picks the best set of genes with the highest classification accuracy. The method has been able to retrieve key genes from multiple data sets that classify with very high accuracy, while being biologically relevant, and no human intervention needed. Initial results provide a high area-under-the-curve with a handful of genes that are already proven to be related to the relevant disease and state based on the latest published medical findings. The proposed methods results provide biomarkers that can be verified in wet lab environments and can then be further analyzed and studied for diagnostic purposes

Comparative Study of Machine Learning Models to Predict PPH

PPH (Postpartum Hemorrhage) is defined as blood loss greater than or equal to 1000 ml following delivery. PPH is among the leading causes of maternal death; however, the existing predictive mechanism used by UNC-CH hospital is oversensitive by flagging too many patients as high risk and is generally abandoned by medical providers. This study is aimed to applying the trending machine learning classifying models to better predict the risk of PPH. Actual dataset was extracted and integrated from EHRS (Electronic Health Record System) with 12 variables considered to be highly relevant to PPH occurrence. Six machine learning models including Logistic Regression, Decision Trees, Random Forest, KNN, SVM and ANN (a deep learning model) were tried and compared in terms of their predicting accuracy and other metrics such as precision and recall. Random Forest stood out as the best model with the accuracy being 89%.Master of Scienc

Contributions to information extraction for spanish written biomedical text

285 p.Healthcare practice and clinical research produce vast amounts of digitised, unstructured data in multiple languages that are currently underexploited, despite their potential applications in improving healthcare experiences, supporting trainee education, or enabling biomedical research, for example. To automatically transform those contents into relevant, structured information, advanced Natural Language Processing (NLP) mechanisms are required. In NLP, this task is known as Information Extraction. Our work takes place within this growing field of clinical NLP for the Spanish language, as we tackle three distinct problems. First, we compare several supervised machine learning approaches to the problem of sensitive data detection and classification. Specifically, we study the different approaches and their transferability in two corpora, one synthetic and the other authentic. Second, we present and evaluate UMLSmapper, a knowledge-intensive system for biomedical term identification based on the UMLS Metathesaurus. This system recognises and codifies terms without relying on annotated data nor external Named Entity Recognition tools. Although technically naive, it performs on par with more evolved systems, and does not exhibit a considerable deviation from other approaches that rely on oracle terms. Finally, we present and exploit a new corpus of real health records manually annotated with negation and uncertainty information: NUBes. This corpus is the basis for two sets of experiments, one on cue andscope detection, and the other on assertion classification. Throughout the thesis, we apply and compare techniques of varying levels of sophistication and novelty, which reflects the rapid advancement of the field

Análisis morfogeométrico de la estructura hemiesférica del segmento anterior del ojo humano y su aplicación clínica

[SPA] Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones.La superficie refractiva más importante del ojo humano es la córnea, que presenta una estructura de forma hemiesférica localizada en el segmento ocular anterior. Esta estructura, incluso en un escenario no patológico, no es perfecta, dado que presenta asimetrías que provocan deformaciones, desalineamientos y descentramientos entre ambos ojos del mismo individuo. Cuando además existen patologías corneales, como pueden ser las ectasias, esta asimetría, y por tanto sus efectos, se acentúan, provocando en el paciente un deterioro ciertamente importante de la capacidad visual, lo que da cuenta de la importancia de disponer de sistemas que permitan una caracterización corneal precisa que facilite la detección, diagnóstico y clasificación de las ectasias. En esta tesis doctoral se ha propuesto un sistema integrado capaz de detectar (fase preclínica) y diagnosticar (fase clínica), de manera eficiente y desde un punto de vista óptico-geométrico, la progresión de la ectasia corneal, permitiendo de esta forma incidir de manera directa en el proceso de toma de decisiones relativas a la calidad visual de los pacientes. Para ello, y partiendo de un modelo sólido personalizado en 3D generado con herramientas de Diseño Asistido por Ordenador, se han propuesto diversos parámetros morfogeométricos macroscópicos de tipo lineal, superficial, volumétrico y angular, con el objetivo de caracterizar la progresión de la ectasia corneal más importante, el denominado queratocono. La estructura microscópica también se ha estudiado, programando una aplicación que permite cuantificar el polimegatismo y el pleomorfismo de las células endoteliales corneales. Posteriormente, se han revisado los distintos sistemas univariantes y multivariantes de diagnóstico y clasificación del queratocono, y se ha comprobado que los parámetros morfogeométricos permiten tanto la detección como la clasificación del queratocono en base a su grado de severidad de acuerdo con las escalas RETICS, Amsler-Krumeich y Alió-Shabayek. Además, se han propuesto dos modelos predictivos (demográfico-óptico-geométricos) de clasificación del grado de la enfermedad en base a la escala RETICS, que han dado lugar al desarrollo de dos aplicaciones informáticas denominadas EMKLAS y KERATOSCORE, que a su vez dan cuenta de la dificultad inherente a la detección de esta enfermedad en la fase preclínica. Por último, se ha aplicado un modelo sólido personalizado obtenido mediante impresión 3D a la educación del paciente, comprobándose que la percepción visual y táctil del modelo permite a los pacientes entender mucho mejor su enfermedad y el tratamiento indicado para ella, mejorando la percepción de calidad del servicio prestado en las clínicas oftalmológicas.[ENG] This doctoral dissertation has been presented in the form of thesis by publication. The most important refractive surface of the human eye is the cornea, which has a hemispherical-shaped structure located in the anterior ocular segment. This structure, even in a non-pathological scenario, is not perfect, since it presents asymmetries that cause deformations, misalignments and decenterings between the two eyes of the same individual. When there are also corneal pathologies, such as ectasias, this asymmetry, and therefore its effects, are accentuated, causing in the patient a certainly significant deterioration of the visual capacity, something that shows the importance of having systems that allow precise corneal characterization to facilitate the detection, diagnosis and classification of ectasias. In this doctoral thesis, an integrated system capable of efficiently detecting (preclinical phase) and diagnosing (clinical phase), from an optical-geometric point of view, the progression of corneal ectasia, has been proposed, thus allowing a direct impact on the decision-making process concerning the visual quality of patients. To do so, and starting from a solid model customized in 3D generated with tools of Computer Assisted Design, several macroscopic morphogeometrical parameters of linear, superficial, volumetric and angular type have been proposed, with the aim of characterizing the progression of the most important corneal ectasia, the so-called keratoconus. The microscopic structure has also been studied, having programmed an application that allows to quantify the polymegatism and pleomorphism of the endothelial corneal cells. Subsequently, several univariate and multivariate diagnostic and classification systems for keratoconus have been revised, and it has been proven that morphogeometrical parameters allow both the detection and classification of keratoconus, basing on its degree of severity according to the RETICS, Amsler-Krumeich and Alió-Shabayek scales. In addition, two classification predictive models (demographical-optical-geometrical) of the disease degree based on the RETICS scale have been proposed, resulting in the development of two computer applications called EMKLAS and KERATOSCORE, which show the inherent difficulty of detecting this disease in its preclinical phase. Finally, a 3D printed personalized solid model has been applied to patient’s education, showing that the visual and tactile perception of the model allows patients to better understand their illness and the treatment indicated for it, improving the perception of quality of service provided in ophthalmological clinics.Las investigaciones que componen esta tesis doctoral se han llevado a cabo con el apoyo de la Red Temática para la Investigación Cooperativa en Salud (RETICS), referencia RD16/0008/0012, y han sido financiadas por el Instituto de Salud Calos III – Subdirección General de Redes y Centros de Investigación Cooperativa (Plan Nacional de I+D+I 2013-2016), el Fondo Europeo de Desarrollo Regional (FEDER), y el programa de Valorización de Resultados de la Universidad Politécnica de Cartagena (PROVALOR-UPCT).Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones. Está formada por un total de doce documentos, todos ellos previamente publicados o aceptados para publicación. En concreto, 9 corresponden a artículos en revistas listadas en el ISI-JCR del Sciences Citation Index, mientras que los 3 restantes corresponden a capítulos de libro de la editorial Springer (Ranking Scholarly Publishers Indicators 4/200). Dichos documentos se enumeran a continuación de acuerdo con el orden cronológico en que han sido publicados durante el desarrollo de la investigación: 1. Cavas-Martínez F, Fernández-Pacheco DG, Cañavate FJF, Velázquez-Blázquez JS, Bolarín JM, Alió JL. Study of Morpho-Geometric Variables to Improve the Diagnosis in Keratoconus with Mild Visual Limitation. Symmetry. 2018;10(8):306. DOI: 10.3390/sym10080306. 2. Cavas-Martinez F, Fernandez-Pacheco DG, Canavate FJF, Velázquez-Blázquez JS, Bolarín J, Tiveron M, Alió J. Early keratoconus detection by patient-specific 3D modelling and geometric parameters analysis. Dyna. 2019. 94(2). DOI: 10.6036/8895. 3. Velázquez-Blázquez JS, Cavas-Martínez F, Alió del Barrio J, Fernandez-Pacheco DG, Cañavate FJF, Parras-Burgos D, Alió J. Detection of Subclinical Keratoconus Using Biometric Parameters. In: Rojas I., Valenzuela O., Rojas F., Ortuño F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science, vol 11466. Springer, Cham. DOI: 10.1007/978-3-030-17935-9_44. 4. Velázquez JS, Cavas F, Alió Del Barrio J, Fernández-Pacheco DG, Alió J. Assessment of the Association between In Vivo Corneal Morphogeometrical Changes and Keratoconus Eyes with Severe Visual Limitation. J Ophthalmol. 2019;2019:8731626-8731626. DOI: 10.1155/2019/8731626. 5. Velázquez-Blázquez JS, Cavas-Martínez F, Campuzano VA, Alió del Barrio J, Cañavate FJF, Alió J. Automatic image processing applied to corneal endothelium cell count and shape characterization. Dyna. 2020;95(2). DOI: 10.6036/9275. 6. Velázquez-Blázquez JS, Fernández-Pacheco DG, Alió del Barrio J, Alió JL, Cavas-Martínez F. Efficacy of Morpho-Geometrical Analysis of the Corneal Surfaces in Keratoconus Disease According to Moderate Visual Limitation. In: Cavas-Martínez F., Sanz-Adan F., Morer Camo P., Lostado Lorza R., Santamaría Peña J. (eds) Advances in Design Engineering. INGEGRAF 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. 10.1007/978-3-030-41200-5_29. 7. Velázquez JS, Cavas F, Bolarín JM, Alió JL. 3D Printed Personalized Corneal Models as a Tool for Improving Patient’s Knowledge of an Asymmetric Disease. Symmetry. 2020;12(1):151. 10.3390/sym12010151. 8. Bolarín JM, Cavas F, Velázquez JS, Alió JL. A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development. Applied Sciences. 2020;10(5):1874. 10.3390/app10051874. 9. Velázquez JS, Cavas F, Bolarín JM, Alió JL. Comparison of Corneal Morphologic Parameters and High Order Aberrations in Keratoconus and Normal eyes. In: Rojas I., Valenzuela O., Rojas F., Ortuño F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science, vol 12108. Springer, Cham. DOI: 10.1007/978-3-030-45385-5_8. 10. Toprak I, Cavas F, Velázquez JS, Alió del Barrio JL, Alió JL. Subclinical keratoconus detection with three-dimensional (3-D) morphogeometric and volumetric analysis. Acta Ophthalmologica. (in press). 10.1111/aos.14433. 11. Velázquez JS, Cavas F, Piñero DP, Cañavate FJF, Alió del Barrio J, Alió JL. Morphogeometric analysis for characterization of keratoconus considering the spatial localization and projection of apex and minimum corneal thickness point. Journal of Advanced Research. (in press). DOI: 10.1016/j.jare.2020.03.012. 12. Velázquez-Blázquez JS, Bolarín JM, Cavas-Martínez F, Alió JL. EMKLAS: A New Automatic-Scoring System for Early and Mild Keratoconus Detection. Translational Vision Science & Technology. (in press). (2020).Escuela Internacional de Doctorado de la Universidad Politécnica de CartagenaUniversidad Politécnica de CartagenaPrograma de Doctorado en Tecnologías Industriale

A Rewriting Logic Approach to Stochastic and Spatial Constraint System Specification and Verification

This paper addresses the issue of specifying, simulating, and verifying reactive systems in rewriting logic. It presents an executable semantics for probabilistic, timed, and spatial concurrent constraint programming ---here called stochastic and spatial concurrent constraint systems (SSCC)--- in the rewriting logic semantic framework. The approach is based on an enhanced and generalized model of concurrent constraint programming (CCP) where computational hierarchical spaces can be assigned to belong to agents. The executable semantics faithfully represents and operationally captures the highly concurrent nature, uncertain behavior, and spatial and epistemic characteristics of reactive systems with flow of information. In SSCC, timing attributes ---represented by stochastic duration--- can be associated to processes, and exclusive and independent probabilistic choice is also supported. SMT solving technology, available from the Maude system, is used to realize the underlying constraint system of SSCC with quantifier-free formulas over integers and reals. This results in a fully executable real-time symbolic specification that can be used for quantitative analysis in the form of statistical model checking. The main features and capabilities of SSCC are illustrated with examples throughout the paper. This contribution is part of a larger research effort aimed at making available formal analysis techniques and tools, mathematically founded on the CCP approach, to the research community.Comment: arXiv admin note: text overlap with arXiv:1805.0743

Jahresbericht Forschung und Transfer 2019

Forschungsjahresbericht 2019 der Hochschule Konstanz Technik, Wirtschaft und Gestaltun

An efficient CNN-BiLSTM model for multi-class intracranial hemorrhage classification

Intracranial hemorrhage (ICH) refers to a type of bleeding that occurs within the skull. ICH may be caused by a wide range of pathology, including, trauma, hypertension, cerebral amyloid angiopa- thy, and cerebral aneurysms. Different subtypes of ICH exist based on their location in the brain, including epidural hemorrhage (EDH), subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), intraventricular hemorrhage (IVH), and intraparenchymal hemorrhage (IPH). Prompt de- tection and management of ICH are crucial as it is a life-threatening medical emergency with high morbidity and mortality rates. Despite accounting for only 10-15% of all strokes, ICH is respon- sible for over 50% of stroke-related deaths. Therefore, the presence, type, and location of an ICH must be immediately diagnosed so that the patients can receive medical intervention. However, accurately identifying ICH in CT slices can be challenging due to the brain’s complex anatomy and the variability in hemorrhage appearance. [...

Micro-hotplate based CMOS sensor for smart gas and odour detection

Low cost, highly sensitive, miniature CMOS micro-hotplate based gas sensors have received great attention recently. The global sensor market is expanding rapidly with an expected increase of 5 ~ 8% grow thin the next five years. The application areas for a gas sensor include but are not limited to, air quality monitoring, industrial and laboratory conditions, military, and biomedical sectors. It is the key hardware component of an electronic nose, as well as the signal processing on the software side. In this thesis, both aspects of such a system were studied with new sensor technologies and improved signal processing algorithms. In addition, this thesis also described different applications and research projects using these sensor technologies and algorithms. A novel plasmonic structure was employed as an infrared source for anon- dispersive infrared gas sensor. This structure was based on a CMOS micro hot plate with three metal layers and periodic cylindrical dots to induce plasmon resonance, that allowed a tunable narrow band infrared radiation with high sensitivity and selectivity. Five gases were studied as target gases, namely, carbon monoxide, carbon dioxide, acetone, ammonia and hydrogen sulfide. These emitter sources were fabricated and characterised with a gascell, optical filters and commercial detectors under different gas concentrations and humidity levels. The results were promising with the lowest detection limit for ammonia at 10 ppm with 5 ppm resolution. On the data processing side, various signal processing methods were explored both on-board and on-board. Temperature modulation was the on-board method by switching the operating temperatures of a micro hotplate. This technique was proven to over come and reduce some typical sensor issues, such as drift, slow re-sponse/recovery speed (from tens of seconds to a few seconds) and even cross sensitivities. Off-board post processing methods were also studied, including principal component analysis, k-nearest neighbours, self-organising maps and shallow/deep neural networks. The results from these algorithms were compared and overall an 85% or higher classification accuracy could be achieved. This work showed the potential to discriminate gases/odours, which could lead to the development of a real-time discrimination algorithm for low cost wearable devices