Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Development and application of new machine learning models for the study of colorectal cancer

En la actualidad, en el ámbito sanitario, hay un interés creciente en la consideración de técnicas de Inteligencia Artificial, en concreto técnicas de Aprendizaje Automático o Machine Learning, que tan buenos resultados están proporcionando desde hace tiempo en diferentes ámbitos, como la industria, el comercio electrónico, la educación, etc. Sin embargo, en el ámbito de la sanidad hay un reto aún mayor ya que, además de necesitar sistemas muy probados, puesto que sus resultados van a repercutir directamente en la salud de las personas, también es necesario alcanzar un buen equilibrio en cuanto a interpretabilidad. Esto es de gran importancia ya que, actualmente, con métodos de caja negra, que pueden llegar a ser muy precisos, es difícil saber qué motivó que el sistema automático tomara una decisión y no otra. Esto puede generar rechazo entre los profesionales sanitarios debido a la inseguridad que pueden llegar a sentir por no poder explicar una decisión clínica tomada en base a un sistema de apoyo a la toma de decisiones. En este contexto, desde el primer momento establecimos que la interpretabilidad de los resultados debía ser una de las premisas que gobernara transversalmente todo el trabajo que se desarrollara en esta tesis doctoral. En este sentido, todos los desarrollos realizados generan bien árboles de clasificación (los cuales dan lugar a reglas interpretables) o bien reglas de asociación que describen relaciones entre los datos existentes. Por otro lado, el cáncer colorrectal es una neoplasia maligna con una alta morbimortalidad tanto en hombres como en mujeres. Esta requiere, indiscutiblemente, de una atención multidisciplinar en la que diferentes profesionales sanitarios (médicos de familia, gastroenterólogos, radiólogos, cirujanos, oncólogos, farmacéuticos, personal de enfermería, etc.) realicen un abordaje conjunto de la patología para ofrecer la mejor atención posible al paciente. Pero además, en adelante, sería muy interesante incorporar a científicos de datos en ese equipo multidisciplinar, ya que se puede sacar un gran partido a toda la información que se genera diariamente sobre esta patología. En esta tesis doctoral se ha planteado, precisamente, el estudio de un conjunto de datos de pacientes con cáncer colorrectal con un un conjunto de técnicas de inteligencia artificial y el desarrollo de nuevos modelos de aprendizaje automático para el mismo. Los resultados han sido los que se exponen a continuación: Una revisión bibliográfica sobre el uso de Machine Learning aplicado a cáncer colorrectal, a partir de la cual se ha realizado una taxonomía de los trabajos existentes a fecha de realización del estudio del estado del arte. Esta taxonomía clasifica los diferentes trabajos estudiados atendiendo a diferentes criterios como son el tipo de dataset utilizado, el tipo de algoritmo implementado, el tamaño del dataset y su disponibilidad pública, el uso o no de algoritmos de selección de características y el uso o no de técnicas de extracción de características. Un modelo de extracción de reglas de asociación de clases con la intención de entender mejor por qué algunos pacientes podrían sufrir complicaciones tras una intervención quirúrgica o recidivas de su cáncer. Este trabajo ha dado lugar a una metodología para la obtención de descripciones interpretables y manejables (es importante que las reglas generadas tengan un tamaño reducido de manera que así sea útil para los sanitarios). Un modelo de selección de características y de instancias para poder inducir mejores árboles de clasificación. Un algoritmo de Evolución Gramatical para inducir una gran variedad de árboles de clasificación tan precisos como los obtenidos por los conocidos métodos C4.5 y CART. En este caso, se ha utilizado la librería PonyGE2 de Python y, debido a su escasa especificidad para aplicación a nuestro problema, se han desarrollado una serie de operadores que permiten inducir árboles más interpretables en comparación con los que produce PonyGE2 de forma estándar. Los resultados obtenidos en cada uno de los desarrollos realizados se han comparado con los resultados proporcionados por métodos existentes en la literatura y de reconocido prestigio, tanto del campo de la clasificación como del campo de la minería de reglas de asociación, demostrándose una mejor adaptación de nuestros modelos a las características que presentaba el conjunto de datos de estudio, y que pueden ser de aplicación a otros casos.Today, in healthcare, there is a growing interest in considering Artificial Intelligence techniques, specifically Machine Learning techniques, which have been providing good results in different fields such as industry, e‑commerce, education, etc., since a long time ago. However, in the field of healthcare there is an even greater challenge because it is needed both highly tested systems, since their results will have a direct impact on people's health, and a good level in terms of interpretability. This is very important since with black box methods, which can be very precise, it will be dificult to know what motivated the automatic system to take one decision or any other. This fact can generate rejection among healthcare professionals due to the insecurity they may feel because they cannot explain a clinical decision taken on the basis of a decision support system. In this context, from the very begining we established that the interpretability of the results should be one of the premises leading all the work carried out in this doctoral thesis. In this sense, all the developments carried out generate either classification trees (which produce interpretable rules) or association rules that describe relationships between existing data. On the other hand, colorectal cancer is a malignant neoplasia with a high morbidity and mortality rate in both men and women, which unquestionably requires multidisciplinary care in which different healthcare professionals (family doctors, gastroenterologists, radiologists, surgeons, oncologists, pharmacists, nursing staff, etc.) take a joint approach to the pathology in order to offer the best possible care to the patient. But it would also be very interesting to incorporate data scientists into this multidisciplinary team in the future, as they can make the most of all the information that is generated on this pathology daily. In this doctoral thesis, it has been proposed the study of a dataset of patients with colorectal cancer with a set of artificial intelligence techniques and the development of new machine learning models for it. The results are shown below: A literature review on the use of Machine Learning applied to colorectal cancer, from which a taxonomy of the existing works has been produced. This taxonomy classifies the different works of the state‑of‑the‑arte according to different criterio such as the type of dataset that has been used, the type of algorithm that has been implemented, the size of the dataset and its public availability, the use or not of feature selection algorithms and the use or not of feature extraction techniques. A class association rule extraction model with the intention of better understanding why some patients might experience complications after surgery or recurrence of their cancer. This work has given rise to a methodology for obtaining interpretable and manageable descriptions (it is important that the generated rules have a reduced size so that they are useful for practitioners). A feature and instance selection model to induce better classification trees. A Grammatical Evolution algorithm to induce a wide variety of classification trees as accurate as those obtained by the well‑known C4.5 and CART methods. In this case, the PonyGE2 Python library has been used and, due to its low specificity for application to our problem, a series of operators have been developed, which allow inducing more interpretable trees compared to those produced by PonyGE2 in a standard way. The results obtained in each of the developments carried out have been compared with the results provided by well known methods existing in the literature, both in the field of classification and in the field of association rule mining, demonstrating a better fit of our models to the features of the dataset, which can be applied to other cases. great efficiency in our models. This demonstrates that it is possible to reach a good balance between precision and interpretability

Big Data Analytics and Information Science for Business and Biomedical Applications

The analysis of Big Data in biomedical as well as business and financial research has drawn much attention from researchers worldwide. This book provides a platform for the deep discussion of state-of-the-art statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions are showcased

Synthetic Biology

Synthetic biology gives us a new hope because it combines various disciplines, such as genetics, chemistry, biology, molecular sciences, and other disciplines, and gives rise to a novel interdisciplinary science. We can foresee the creation of the new world of vegetation, animals, and humans with the interdisciplinary system of biological sciences. These articles are contributed by renowned experts in their fields. The field of synthetic biology is growing exponentially and opening up new avenues in multidisciplinary approaches by bringing together theoretical and applied aspects of science

Protein-protein docking for interactomic studies and its aplication to personalized medicine

[eng] Proteins are the embodiment of the message encoded in the genes and they act as the building blocks and effector part of the cell. From gene regulation to cell signalling, as well as cell recognition and movement, protein-protein interactions (PPIs) drive many important cellular events by forming intricate interaction networks. The number of all non-redundant human binary interactions, forming the so-called interactome, ranges from 130,000 to 650,000 interactions as estimated by different studies. In some diseases, like cancer, these PPIs are altered by the presence of mutations in individual proteins, which can change the interaction networks of the cell resulting in a pathological state. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. To understand how these mutations can alter the PPIs, we need to look at the three-dimensional structure of the protein complexes at the atomic level. However, there are available structures for less than 10% of the estimated human interactome. Computational approaches such as protein-protein docking can help to extend the structural coverage of known PPIs. In the protein-protein docking field, rigid-body docking is a widely used docking approach, since es fast, computationally cheap and is often capable of generating a pool of models within which a near-native structure can be found. These models need to be scored in order to select the acceptable ones from the set of poses. In the present thesis, we have characterized the synergy between combination of protein-protein docking methods and several scoring functions. Our findings provide guides for the use of the most efficient scoring function for each docking method, as well as instruct future scoring functions development efforts Then we used docking calculations to predict interaction hotspots, i.e. residues that contribute the most to the binding energy, and interface patches by including neighbour residues to the predictions. We developed and validated a method, based in the Normalize Interface Propensity (NIP) score. The work of this thesis have extended the original NIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This methodology was also applied to predict the location of 14,551 nsSNPs in 4,254 proteins, for more than 12,000 interactions without 3D structure. We found that 34% of the disease-associated nsSNPs were located at a protein-protein interface. This opens future opportunities for the high-throughput characterization of pathological mutations at the atomic level resolution, and can help to design novel therapeutic strategies to re-stabilize the affected PPIs by disease-associated nsSNPs

Advances and Novel Treatment Options in Metastatic Melanoma

The book presents several studies reporting advances on melanoma pathogenesis, diagnosis and therapy. It represents a milestone on the state of the art, updated at 2021, and also presents the current knowledge on the future developments in melanoma field