FLIR vs SEEK thermal cameras in biomedicine: comparative diagnosis through infrared thermography

Background: In biomedicine, infrared thermography is the most promising technique among other conventional methods for revealing the differences in skin temperature, resulting from the irregular temperature dispersion, which is the significant signaling of diseases and disorders in human body. Given the process of detecting emitted thermal radiation of human body temperature by infrared imaging, we, in this study, present the current utility of thermal camera models namely FLIR and SEEK in biomedical applications as an extension of our previous article. Results: The most significant result is the differences between image qualities of the thermograms captured by thermal camera models. In other words, the image quality of the thermal images in FLIR One is higher than SEEK Compact PRO. However, the thermal images of FLIR One are noisier than SEEK Compact PRO since the thermal resolution of FLIR One is 160 × 120 while it is 320 × 240 in SEEK Compact PRO. Conclusion: Detecting and revealing the inhomogeneous temperature distribution on the injured toe of the subject, we, in this paper, analyzed the imaging results of two different smartphone-based thermal camera models by making comparison among various thermograms. Utilizing the feasibility of the proposed method for faster and comparative diagnosis in biomedical problems is the main contribution of this study.The work and the contribution were supported by the SPEV project “Smart Solutions in Ubiquitous Computing Environments”, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-SPEV-2019). Supported by the grant TIN2016-75850-R from the Spanish Ministry of Economy and Competitiveness with FEDER funds

cMRI-BED: A novel informatics framework for cardiac MRI biomarker extraction and discovery applied to pediatric cardiomyopathy classification

Background\ud Pediatric cardiomyopathies are a rare, yet heterogeneous group of pathologies of the myocardium that are routinely examined clinically using Cardiovascular Magnetic Resonance Imaging (cMRI). This gold standard powerful non-invasive tool yields high resolution temporal images that characterize myocardial tissue. The complexities associated with the annotation of images and extraction of markers, necessitate the development of efficient workflows to acquire, manage and transform this data into actionable knowledge for patient care to reduce mortality and morbidity.\ud \ud Methods\ud We develop and test a novel informatics framework called cMRI-BED for biomarker extraction and discovery from such complex pediatric cMRI data that includes the use of a suite of tools for image processing, marker extraction and predictive modeling. We applied our workflow to obtain and analyze a dataset of 83 de-identified cases and controls containing cMRI-derived biomarkers for classifying positive versus negative findings of cardiomyopathy in children. Bayesian rule learning (BRL) methods were applied to derive understandable models in the form of propositional rules with posterior probabilities pertaining to their validity. Popular machine learning methods in the WEKA data mining toolkit were applied using default parameters to assess cross-validation performance of this dataset using accuracy and percentage area under ROC curve (AUC) measures.\ud \ud Results\ud The best 10-fold cross validation predictive performance obtained on this cMRI-derived biomarker dataset was 80.72% accuracy and 79.6% AUC by a BRL decision tree model, which is promising from this type of rare data. Moreover, we were able to verify that mycocardial delayed enhancement (MDE) status, which is known to be an important qualitative factor in the classification of cardiomyopathies, is picked up by our rule models as an important variable for prediction.\ud \ud Conclusions\ud Preliminary results show the feasibility of our framework for processing such data while also yielding actionable predictive classification rules that can augment knowledge conveyed in cardiac radiology outcome reports. Interactions between MDE status and other cMRI parameters that are depicted in our rules warrant further investigation and validation. Predictive rules learned from cMRI data to classify positive and negative findings of cardiomyopathy can enhance scientific understanding of the underlying interactions among imaging-derived parameters

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

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection

Background: Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. Methods: This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. Results and conclusion: The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed

Formal Concept Analysis and Knowledge Integration for Highlighting Statistically Enriched Functions from Microarrays Data

International audienceIn this paper we introduce a new method for extracting enriched biological functions from transcriptomic databases using an integrative bi-classication approach. The initial gene datasets are firstly represented as a formal context (objects attributes), where objects are genes, and attributes are their expression profiles and complementary information of different knowledge bases. After that, Formal Concept Analysis (FCA) is applied for extracting formal concepts regrouping genes having similar transcriptomic profiles and functional behaviors. An enrichment analysis is then performed in order to identify the pertinent formal concepts from the generated Galois lattice, and to extract biological functions that could participate in the proliferation of cancers

Multi-Head Graph Convolutional Network for Structural Connectome Classification

We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, capturing representations from the input data thoroughly. To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification. This quantifies the degree to which the connectome varies depending on the sex, which is important for improving our understanding of health and disease in both sexes. We show experiments on two publicly available datasets: PREVENT-AD (347 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model

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

Time-energy Analysis of Multilevel Parallelism in Heterogeneous Clusters: the Case of EEG Classification in BCI Tasks

Present heterogeneous architectures interconnect nodes including multiple multi-core microprocessors and accelerators that allow different strategies to accelerate the applications and optimize their energy consumption according to the specific power-performance trade-offs. In this paper, a multi-level parallel procedure is proposed to take advantage of all nodes of a heterogeneous CPU-GPU cluster. Two more alternatives have been implemented, and experimentally compared and analyzed from both running time and energy consumption. Although the paper considers an evolutionary master-worker algorithm for feature selection in EEG classification, the conclusions from the experimental analysis here provided can be frequently applied, as many other useful bioinformatics and data mining applications show the same master-worker profile than the classification problem here considered. Our parallel approach allows to reduce the time by a factor of up to 83, with only about a 4.9% of energy consumed by the sequential procedure, in a cluster with 36 CPU cores and 43 GPU compute units.Spanish Ministerio de Ciencia, Innovación y Universidades under grant PGC2018-098813-B-C31ERDF fun