Network-based methods for biological data integration in precision medicine

[eng] The vast and continuously increasing volume of available biomedical data produced during the last decades opens new opportunities for large-scale modeling of disease biology, facilitating a more comprehensive and integrative understanding of its processes. Nevertheless, this type of modelling requires highly efficient computational systems capable of dealing with such levels of data volumes. Computational approximations commonly used in machine learning and data analysis, namely dimensionality reduction and network-based approaches, have been developed with the goal of effectively integrating biomedical data. Among these methods, network-based machine learning stands out due to its major advantage in terms of biomedical interpretability. These methodologies provide a highly intuitive framework for the integration and modelling of biological processes. This PhD thesis aims to explore the potential of integration of complementary available biomedical knowledge with patient-specific data to provide novel computational approaches to solve biomedical scenarios characterized by data scarcity. The primary focus is on studying how high-order graph analysis (i.e., community detection in multiplex and multilayer networks) may help elucidate the interplay of different types of data in contexts where statistical power is heavily impacted by small sample sizes, such as rare diseases and precision oncology. The central focus of this thesis is to illustrate how network biology, among the several data integration approaches with the potential to achieve this task, can play a pivotal role in addressing this challenge provided its advantages in molecular interpretability. Through its insights and methodologies, it introduces how network biology, and in particular, models based on multilayer networks, facilitates bringing the vision of precision medicine to these complex scenarios, providing a natural approach for the discovery of new biomedical relationships that overcomes the difficulties for the study of cohorts presenting limited sample sizes (data-scarce scenarios). Delving into the potential of current artificial intelligence (AI) and network biology applications to address data granularity issues in the precision medicine field, this PhD thesis presents pivotal research works, based on multilayer networks, for the analysis of two rare disease scenarios with specific data granularities, effectively overcoming the classical constraints hindering rare disease and precision oncology research. The first research article presents a personalized medicine study of the molecular determinants of severity in congenital myasthenic syndromes (CMS), a group of rare disorders of the neuromuscular junction (NMJ). The analysis of severity in rare diseases, despite its importance, is typically neglected due to data availability. In this study, modelling of biomedical knowledge via multilayer networks allowed understanding the functional implications of individual mutations in the cohort under study, as well as their relationships with the causal mutations of the disease and the different levels of severity observed. Moreover, the study presents experimental evidence of the role of a previously unsuspected gene in NMJ activity, validating the hypothetical role predicted using the newly introduced methodologies. The second research article focuses on the applicability of multilayer networks for gene priorization. Enhancing concepts for the analysis of different data granularities firstly introduced in the previous article, the presented research provides a methodology based on the persistency of network community structures in a range of modularity resolution, effectively providing a new framework for gene priorization for patient stratification. In summary, this PhD thesis presents major advances on the use of multilayer network-based approaches for the application of precision medicine to data-scarce scenarios, exploring the potential of integrating extensive available biomedical knowledge with patient-specific data

Generación de un programa bioinformático de ayuda diagnóstica para enfermedades del sistema inmunitario de base genética

Históricamente, la medicina clínica ha tendido a definir las enfermedades en base a sus signos clínicos, dejando de lado las características funcionales de los genes mutados causales, algo de lo que, hasta la explosión de la biología y la genética molecular se tenía un conocimiento muy reducido. Recientemente, la biología de sistemas está reavivando el interés en estudiar la relación global entre la información clínica y molecular de las enfermedades genéticas, algo que es especialmente importante en varios campos de la medicina, como en el diagnóstico de enfermedades genéticas, o la clasificación de las mismas. Mediante herramientas bioinformáticas es posible procesar la enorme y creciente cantidad de información presente en las distintas bases de datos de enfermedades y genes causales de las mismas. En este trabajo se presenta una herramienta bioinformática en el lenguaje de programación estadístico R para la ayuda diagnóstica de enfermedades del sistema inmune de base genética, más concretamente de la enfermedad celíaca. El programa posee una interfaz gráfica de utilización intuitiva para el usuario final y es capaz de procesar la información de la genética HLA del paciente para calcular el riesgo asociado a la genética de padecer la enfermedad.Máster en Investigación Biomédic

La inteligencia artifical en biomedicina: oportunidades y desafíos

Sección: A fondo[ES] “¿Las máquinas pueden pensar?”. Esta pregunta se la formuló Alan Turing1, considerado como el padre de la computación, ya en el año 1950 (Turing, 1950). Al mismo tiempo, formuló un pequeño juego al que acuñó el nombre de “juego de las imitaciones”. El juego consiste en que una persona A interactúa con una máquina B y una persona C, e intentará adivinar cuál de ellos es la máquina. A no tiene acceso visual ni sonoro a B ni a C: sólo se puede comunicar a través de una terminal con ambas. El nombre de “imitación” se refiere a que la máquina B intentará replicar el comportamiento de la persona C. Este juego ha pasado a conocerse como el Test de Turing, que además ha extendido la idea básica del juego de las imitaciones: ¿podemos distinguir entre una persona y una máquina, por ejemplo, durante una conversación por mensajes de texto? ¿Y durante una llamada telefónica? Puede que un día tengamos incluso que preguntarnos si podemos distinguir a una persona de un robot