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Background To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. Results The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. Conclusions In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F1 score in evaluating binary classification tasks by all scientific communities.

Background Population attributable fractions (PAF) measure the proportion of disease prevalence that would be avoided in a hypothetical population, similar to the population of interest, but where a particular risk factor is eliminated. They are extensively used in epidemiology to quantify and compare disease burden due to various risk factors, and directly influence public policy regarding possible health interventions. In contrast to individual specific metrics such as relative risks and odds ratios, attributable fractions depend jointly on both risk factor prevalence and relative risk. The relative contributions of these two components is important, and usually needs to be presented in summary tables that are presented together with the attributable fraction calculation. However, representing PAF in an accessible graphical format, that captures both prevalence and relative risk, may assist interpretation. Methods Taylor-series approximations to PAF in terms of risk factor prevalence and log-odds ratio are derived that facilitate simultaneous representation of PAF, risk factor prevalence and risk-factor/disease log-odds ratios on a single co-ordinate axis. Methods are developed for binary, multi-category and continuous exposure variables. Results The methods are demonstrated using INTERSTROKE, a large international case control dataset focused on risk factors for stroke. Conclusions The described methods could be used as a complement to tables summarizing prevalence, odds ratios and PAF, and may convey the same information in a more intuitive and visually appealing manner. The suggested nomogram can also be used to visually estimate the effects of health interventions which only partially reduce risk factor prevalence. Finally, in the binary risk factor case, the approximations can also be used to quickly convert logistic regression coefficients for a risk factor into approximate PAFs.

The aim of this study was to determine if there is a difference in 6-min walk test (6MWT) distance when the assessor accompanies the patient to continuously measure peripheral oxygen saturation (SpO2) compared to the patient walking unaccompanied. We conducted a randomised crossover study to evaluate the impact of the assessor walking with the patient during the 6MWT (6MWTwith) versus the patient walking alone (6MWTwithout). At the end of a pulmonary rehabilitation programme, each patient performed two 6MWTs in random order and separated by a 30-min rest. 49 patients with chronic obstructive pulmonary disease (COPD) (Global Initiative for Obstructive Lung Disease classification II–IV) were included. In a regression model adjusting for period and subject, accompanying the patient resulted in a lower walking distance (mean difference −9.1 m, 95% CI −13.9– −4.3, p=0.0004). Notably, six patients walked more than 30 m farther (minimal important difference) in one of the two conditions (6MWTwith: n=1, 6MWTwithout: n=5). There were no between-sequence-group differences in heart rate, dyspnoea, leg fatigue and SpO2. The median (interquartile range) number and duration of SpO2 signal artefacts were high but not different between the experimental conditions (6MWTwith: 17 (4–24), 34 s (7–113 s); 6 MWTwithout: 11 (3–26), 24 s (4–62 s)). At a study population level, we observed a statistically significant difference in 6MWT distance between the two experimental conditions; however, the magnitude of difference was small and may not be clinically relevant. Nevertheless, in a clinical setting, unaccompanied walking resulted in a substantially higher walking distance in individual patients, pointing towards strictly standardised testing methodology, in particular in pre–post study designs., Accompanied walking during a 6MWT versus unaccompanied walking results in shorter walked distance (mean difference −9.1 m, 95% CI −13.9– −4.3 m). No differences in nadir SpO2 or oximetry artefacts were noted between accompanied and unaccompanied walking. https://bit.ly/3tBjfCq

In many application fields, data corresponds to curves. This work focuses on the analysis of spectrometric curves, composed of hundreds of ordered variables that corresponds to the absorbance values measured for each wavenumber. In this context, an automatic statistical procedure is developped, that aims at building a prediction model taking into account the heterogeneity of the observed data. More precisely, a diagnosis tool is built in order to predict a metabolic disease from spectrometric curves measured on a population composed of patients with differents profile. The procedure allows to select portions of curves relevant for the prediction and to build a partition of the data and a sparse predictive model simultaneously, using a mixture of penalized regressions suitable for functional data. In order to study the complexity of the data and of the application case, a method to better understand and display the interactions between variables is built. This method is based on the study of the covariance matrix structure, and aims to highlight the dependencies between blocks of variables. A medical example is used to present the method and results, and allows the use of specific visualization tools., Dans de nombreux domaines d’application, les données récoltées correspondent à des courbes. Ce travail se concentre sur l’analyse de courbes de spectrométrie, constituées de plusieurs centaines de variables ordonnées, correspondant chacune à une valeur d’absorbance associée aux nombres d’ondes mesurés. Dans ce contexte, une méthode de traitement statistique automatique est développée, avec pour objectif la construction d’un modèle de prédiction prenant en compte l’hétérogénéité des données observées. Plus particulièrement, un modèle de diagnostic d’une maladie métabolique est établi à partir de courbes mesurées sur des individus provenant d’une population constituée de profils de patients différents. La procédure développée permet de sélectionner l’information pertinente sous forme de portions de courbes discriminantes, puis de construire de façon simultanée une partition des données et un modèle de prédiction parcimonieux grâce à un mélange de régressions pénalisées adapté aux données fonctionnelles. Ces données étant complexes, tout comme le cas d’application étudié, une méthode permettant une meilleure compréhension et une meilleure visualisation des interactions entre les portions de courbes a par ailleurs été développée. Cette méthode se base sur l’étude de la structure des matrices de covariance, avec pour but de faire ressortir des blocs de dépendances entre intervalles de variables. Un cas d’application médicale est utilisé pour présenter la méthode et les résultats, et permet l’utilisation d’outils de visualisation spécifiques.

Interferon-stimulated genes (ISGs) are a set of genes whose transcription is induced by interferon (IFN). The measure of the expression of ISGs enables calculating an IFN score, which gives an indirect estimate of the exposition of cells to IFN-mediated inflammation. The measure of the IFN score is proposed for the screening of monogenic interferonopathies, like the Aicardi-Gouti&egrave, res syndrome, or to stratify subjects with systemic lupus erythematosus to receive IFN-targeted treatments. Apart from these scenarios, there is no agreement on the diagnostic value of the score in distinguishing IFN-related disorders from diseases dominated by other types of cytokines. Since the IFN score is currently measured in several research hospitals, merging experiences could help define the potential of scoring IFN inflammation in clinical practice. However, the IFN score calculated at different laboratories may be hardly comparable due to the distinct sets of IFN-stimulated genes assessed and to different controls used for data normalization. We developed a reliable approach to minimize the inter-laboratory variability, thereby providing shared strategies for the IFN signature analysis and allowing different centers to compare data and merge their experiences.

Aim: The aim of the study was to assess predictors of outcome in patients hospitalized for dilated cardiomyopathy (DCM) and severe left ventricular dysfunction. Patients & methods: 83 pediatric patients hospitalized for heart failure due to DCM with coexistent left ventricular dysfunction were enrolled.Results: Overall, 5-year survival free from heart transplantation was 69.8%. Normalization of left ventricular function was achieved in 39.8% of patients during follow-up: younger age, less necessity of inotropic support and other than idiopathic DCM predicted left ventricular function, while familial history for cardiac disease or sudden death and inotropic support during hospitalization were associated with poorer outcome. Conclusion: Almost 40% of patients with DCM experienced a complete normalization of cardiac function. Outcome was extremely variable according to the type of DCM.

HIV-1 mother-to-child transmission (HIV-1 MTCT), is an important cause of children mortality worldwide. Brazil has been traditionally praised by its HIV/Aids program, which provides free-of-charge care for people living with HIV-1. Using public epidemiology and demographic databases, we aimed at modeling HIV-1 MTCT prevalence in Brazil through the years (1994–2016) and elaborate a statistical model for forecasting, contributing to HIV-1 epidemiologic surveillance and healthcare decision-making. We downloaded sets of live births and mothers’ data alongside HIV-1 cases notification in children one year old or less. Through time series modeling, we estimated prevalence along the years in Brazil, and observed a remarkable decrease of HIV-1 MTCT between 1994 (10 cases per 100,000 live births) and 2016 (five cases per 100,000 live births), a reduction of 50%. Using our model, we elaborated a prognosis for each Brazilian state to help HIV-1 surveillance decision making, indicating which states are in theory in risk of experiencing a rise in HIV-1 MTCT prevalence. Ten states had good (37%), nine had mild (33%), and eight had poor prognostics (30%). Stratifying the prognostics by Brazilian region, we observed that the Northeast region had more states with poor prognosis, followed by North and Midwest, Southeast and South with one state of poor prognosis each. Brazil undoubtedly advanced in the fight against HIV-1 MTCT in the past two decades. We hope our model will help indicating where HIV-1 MTCT prevalence may rise in the future and support government decision makers regarding HIV-1 surveillance and prevention. Keywords: HIV-1, Vertical transmission, Perinatal transmission, Biostatistics, Epidemiology, Forecasting

This review aimed to arrange the concepts of a network meta-analysis (NMA) and to demonstrate the analytical process of NMA using Stata software under frequentist framework. The NMA tries to synthesize evidences for a decision making by evaluating the comparative effectiveness of more than two alternative interventions for the same condition. Before conducting a NMA, 3 major assumptions-similarity, transitivity, and consistency-should be checked. The statistical analysis consists of 5 steps. The first step is to draw a network geometry to provide an overview of the network relationship. The second step checks the assumption of consistency. The third step is to make the network forest plot or interval plot in order to illustrate the summary size of comparative effectiveness among various interventions. The fourth step calculates cumulative rankings for identifying superiority among interventions. The last step evaluates publication bias or effect modifiers for a valid inference from results. The synthesized evidences through five steps would be very useful to evidence-based decision-making in healthcare. Thus, NMA should be activated in order to guarantee the quality of healthcare system.

Genome-wide association studies (GWAS) have resulted in the identification of several dozen of genes and single nucleotide polymorphisms (SNPs) contributing to type 2 diabetes.More generally, GWAS have identified thousands of SNPs contributing to complex diseases in humans.However, the functional characterization and biological mechanisms involving these SNPs and genes remain largely to be explored. Indeed, the consequences of these polymorphisms are complex and little known.One direct consequence of the SNPs is the alteration of the protein encoded by a gene, or even a complete transcriptional gene silencing (e.g. codon stop in the sequence). Furthermore, these polymorphisms may have a regulatory role in gene expression, for example, by interfering with the binding of transcription factors and enzymes involved in DNA methylation.Despite the strong associations of identified SNPs, they cannot explain the full heritability of type 2 diabetes, suggesting interactions mechanisms between the different layers of -omics, such as genomics, transcriptomics and Epigenomics.The shift of paradigm in statistical genetics and the availability of transcriptomic and epigenomic data are responsible for the evolution of the discipline, moving from association studies to multi-omics, and providing insights on the functional aspect of the SNPs or genes involved, and in some cases allowing to evaluate the causal link of these variants on the pathology.The methodological developments and their applications proposed in this thesis are various, ranging from a similar approach to GWAS, leveraging the longitudinal data available in some cohorts (e.g. D.E.S.I.R.), using an joint model approach; the functional characterisation of candidate genes in insulin secretion by a multi tissue transcriptomic study and transcriptomic study in a cell model; the identification of a new candidate gene (PDGFA) involved in the deregulation of the insulin\\\'s pathway in type 2 diabetes through epigenetic and transcriptomic mechanisms; and finally, the characterisation of the effect on the transcriptome of two substitutes of bisphenol A in a primary adipocyte model.The increase of knowledge in biological processes involving SNPs and genes identified by GWAS could enable the development of more effective diagnostic strategies, and the identification of therapeutic targets for the treatment of type 2 diabetes and associated complications (e.g., insulin resistance, NAFLD, cancer, etc.).More generally, these multi-omics studies pave the way for the emerging approach of precision medicine, allowing the treatment and prevention of pathologies while taking into account what makes the specificity of an individual, namely his genome and his environment, both interacting on his transcriptome and his epigenome.; Les études d'association pangénomiques (GWAS) ont permis l'identification de plusieurs dizaines de gènes et de polymorphismes nucléotidiques (SNPs) contribuant au risque de diabète de type 2.Plus généralement, les GWAS ont permis d'identifier des milliers de SNPs contribuant à des maladies complexes chez l'Homme.Cependant, la caractérisation fonctionnelle et les mécanismes biologiques impliquant ces SNPs et ces gènes restent en grande partie à explorer.En effet, les conséquences de ces polymorphismes sont complexes et peu connues. Une conséquence directe est l'altération de la protéine codée par un gène, voire une extinction complète de la transcription du gène (p.ex. via l’introduction d’un codon stop dans la séquence).Par ailleurs, ces polymorphismes peuvent avoir un rôle de régulation dans l'expression des gènes, par exemple, en perturbant la liaison de facteurs de transcription et d'enzymes impliqués dans la méthylation de l'ADN.Malgré des associations fortes des SNPS identifiés, ils ne peuvent expliquer la totalité de l'héritabilité du diabète de type 2, suggérant par le fait même des mécanismes d'interactions entre les différentes couches que représentent la génomique, la transcriptomique et l'épigénomique.Le changement de paradigme en statistique génétique et la disponibilité de données transcriptomiques et épigénomiques sont responsables de l'évolution du domaine, passant des analyses d'associations à des analyses transversales de type multi-omique, et permettant de fournir des éléments de réponse sur l’aspect fonctionnel des SNPs ou des gènes impliqués, et dans certains cas, permettant d'évaluer le lien causal de ces variants sur la pathologie.Les développements et applications méthodologiques proposés dans cette thèse sont variés, allant d’une approche similaire aux GWAS, mettant à profit les données longitudinales disponibles dans certaines cohortes (p.ex. D.E.S.I.R.), au moyen d'un modèle joint; de la caractérisation fonctionnelle de gènes candidats, identifiés par GWAS, dans la sécrétion d'insuline par une étude transcriptomique multi tissu et dans un modèle cellulaire; de l'identification d'un nouveau gène candidat (PDGFA) impliqué dans la dérégulation de la voie de l'insuline dans le diabète de type 2 via des mécanismes épigénétiques et transcriptomiques; et enfin de la caractérisation de l'effet sur le transcriptome de deux substituts du bisphénol A dans un modèle d’adipocyte primaire.L'augmentation des connaissances des processus biologiques dans lesquels sont impliqués les SNPs et gènes identifiés par GWAS pourrait permettre l'élaboration de stratégies diagnostiques plus efficaces, ainsi que l'identification de cibles thérapeutiques pour le traitement du diabète de type 2 et des complications associées (p.ex. insulinorésistance, NAFLD, cancer, etc.). Plus généralement, ces études multi-omiques ouvrent la voie à l'approche émergente que représente la médecine de précision, permettant le traitement et la prévention des pathologies tout en prenant en compte ce qui fait la spécificité d'un individu, à savoir son génome et son environnement, tous deux interagissant sur son transcriptome et son épigénome.

Objective The aim of this study was to establish when a second-stage diagnostic test may be of value in cases where a primary diagnostic test has given an uncertain diagnosis of the benign or malignant nature of an adnexal mass. Methods The diagnostic performance with regard to discrimination between benign and malignant adnexal masses for mathematical models including ultrasound variables and for subjective evaluation of ultrasound findings by an experienced ultrasound examiner was expressed as area under the receiver-operating characteristics curve (AUC), sensitivity and specificity. These were calculated for the total study population of 1938 patients with an adnexal mass as well as for sub-populations defined by the certainty with which the diagnosis of benignity or malignancy was made. The effect of applying a second-stage test to the tumors where risk estimation was uncertain was determined. Results The best mathematical model (LR1) had an AUC of 0.95, sensitivity of 92% and specificity of 84% when applied to all tumors. When model LR1 was applied to the 10% of tumors in which the calculated risk fell closest to the risk cut-off of the model, the AUC was 0.59, sensitivity 90% and specificity 21%. A strategy where subjective evaluation was used to classify these 10% of tumors for which LR1 performed poorly and where LR1 was used in the other 90% of tumors resulted in a sensitivity of 91% and specificity of 90%. Applying subjective evaluation to all tumors yielded an AUC of 0.95, sensitivity of 90% and specificity of 93%. Sensitivity was 81% and specificity 47% for those patients where the ultrasound examiner was uncertain about the diagnosis (n = 115; 5.9%). No mathematical model performed better than did subjective evaluation among the 115 tumors where the ultrasound examiner was uncertain. Conclusion When model LR1 is used as a primary test for discriminating between benign and malignant adnexal masses, the use of subjective evaluation of ultrasound findings by an experienced examiner as a second-stage test in the 10% of cases for which the model yields a risk of malignancy closest to its risk cut-off will improve specificity without substantially decreasing sensitivity. However, none of the models tested proved suitable as a second-stage test in tumors where subjective evaluation yielded an uncertain result. Copyright (C) 2010 ISUOG. Published by John Wiley & Sons, Ltd.

International audience; Owing to the high complexity of diatom community data, there is a special need for methods accounting for complex non-linear gradients. A Kohonen’s self-organizing map (SOM) is a neural network with unsupervised learning. It allows both unbiased classification of the communities and visualization of biological gradients on a two-dimensional plane. However, as with other neural networks, many parameters must be set. A new R-package with a SOM parameterization specifically suited to diatom communities has been developed. Further developments will consist of creating a graphical user interface in order to make this method easier to use for the scientific community.

Tesis Doctoral presentada por Dña. Celia Fontanillo Fontanillo alumna del programa de Doctorado del CiC-IBMCC de la Universidad de Salamanca., [ES]: La presente Tesis Doctoral se enmarca en las áreas de conocimiento de la Bioinformática y Biología Computacional y también de la Genómica Funcional y Genómica del Cáncer. El objetivo fundamental de la Genómica Funcional es entender cómo funciona el genoma en su conjunto mediante el análisis de la actividad de todos sus genes y de los múltiples factores que regulan o influyen la expresión de los mismos, así como otras entidades biomoleculares relacionadas. La recolección sistemática de información y datos procedentes de tecnologías genómicas experimentales globales a gran escala proporciona un punto de partida para desvelar la actividad del genoma y el comportamiento de los sistemas vivos asociado a su genoma. En este marco temático, el trabajo de esta Tesis Doctoral ha sido el desarrollo y aplicación de varios algoritmos bioinformáticos para el análisis de datos sobre muestras humanas de pacientes con cáncer procedentes de diversas plataformas genómicas de alta densidad, así como su integración e interpretación para descubrir los genes y procesos biológicos alterados en dichas patologías., [EN]. The present thesis is part of the knowledge areas of Bioinformatics and Computational Biology and Functional Genomics and Cancer Genomics. The fundamental objective of the Functional Genomics is to understand how the genome works as a whole by analyzing the activity of all genes and the multiple factors that regulate or influence the expression of these and other biomolecular related entities. The systematic collection of information and data from global experimental large-scale genomic technologies provides a starting point to unravel genome activity and behavior of living systems associated genome. The work of this thesis has been the development and implementation of several bioinformatics algorithms for analyzing data on human samples of cancer patients from different genomic platforms high density as well as their integration and interpretation to discover altered genes and biological processes in these diseases.

Objective Two logistic regression models have been developed for the characterization of adnexal masses. The goal of this prospective analysis was to see whether these models perform differently according to the prevalence of malignancy and whether the cut-off levels of risk assessment for malignancy by the models require modification in different centers. Methods Centers were categorized into those with a prevalence of malignancy below 15%, between 15 and 30% and above 30%. The areas under the receiver–operating characteristics curves (AUC) were compared using bootstrapping. The optimal cut-off level of risk assessment for malignancy was chosen per center, corresponding to the highest sensitivity level possible while still keeping a good specificity. Results Both models performed better in centers with a lower prevalence of malignant cases. The AUCs of the two models for centers with fewer than 15% malignant cases were 0.97 and 0.95, those of centers with 15–30% malignancy were 0.95 and 0.93 and those of centers with more than 30% malignant cases were 0.94 and 0.92. This decrease in performance was due mainly to the decrease in specificity from over 90 to around 76%. In the centers with a higher percentage of malignant cases, a sensitivity of at least 90% with a good specificity could not be obtained by choosing a different cut-off level. Conclusions Overall the models performed well in all centers. The performance of the logistic regression models worsened with increasing prevalence of malignancy, due to a case mix with more borderline and complex benign masses seen in those centers. Because the cut-off of 0.10 is optimal for all three types of center, it seems reasonable to use this cut-off for both models in all centers. Copyright © 2011 ISUOG. Published by John Wiley & Sons, Ltd.

Macromolecular structural analysis has been used for a long tune; in particular for proteins, with the aim of correlating three dimensional structures with possible functions and interactions. An innovative approach is to tackle these aspects by working on macromolecular surfaces instead of using amino acid or atomic configurations directly, because protein interaction is mainly driven by the macromolecular external characteristics. However, it is difficult to work with surfaces because applications for molecular visualization lack appropriate functions to manipulate these data structures properly. In this paper we describe a visualization pipeline to represent molecular surfaces in three-dimensional space. The pipeline relies on tire Visualization Toolkit libraries, and provides a high level functions and easy user interface. This software has been developed in the context of a surface. analysis project, but; its classes and methods can be used and integrated in a wide range of applications.

Real-time Polymerase Chain Reaction (RT-PCR) is one of the most sensitive and reliably quantitative methods for measuring gene expression levels. Despite this technique is broadly applied in biomedical sciences, data processing and statistical procedures for the analysis of RT-PCR data still present some lacks in reliability.In our work we analyze a data set from a genetic medicine experiment whose aim is to investigate the expression level of 225 genes in 2 types of cells which are possible candidates for tumour acceleration. In this experiment 2 biological replicates for 15 mice were considered, using 3 different experimental platforms.Our goal is to estimate a model which accounts for the complex design of a RT-PCR experiment, thus including sample effect, gene effect, treatment effect, card effect and interactions. The main interest is the interaction between gene and sample.With respect to standard techniques our model allows for a better estimation of the variance with less probability of non significant result in case of true differences.

One major hurdle in gene therapy is that introducing into host target cells a piece of genetic material (retrovirus) to handle a genetic disease may induce cancer. Tumorigenesis is thus suspected to be caused by insertion process called integration. Investigating whether a genetic area on the genome could be favoured by integration of a retrovirus in gene therapy, is a crucial aspect. These area are called “hot-spot”, or “common integration sites”(CIS). We propose a method for detecting hotspots based on the “Peaks-height distribution”, thus accounting for the integration distribution rather than based on integrations distances only.

La importancia relativa que doce asignaturas básicas o preclínicas tienen para el ejercicio de la medicina general y para la formación científica del médico es estudiada con el método de comparaciones apareadas y con el de intervalos semejantes. Los grupos estudiados son profesores de medicina, estudiantes del último curso de la carrera y médicos en ejercicio. De acuerdo con su importancia para el ejercicio de la medicina general, podemos distinguir tres grupos de asignaturas. Patología general, Fisiología y Farmacología constituyen el grupo de máxima relevancia. Biología médica, Bioestadística y Física médica integran el grupo de asignaturas menos relevantes. El resto de las asignaturas forman un grupo intermedio. Respecto a su importancia para la formación científica del médico existen pequeñas diferencias en la composición de los grupos. A judgment of the relevance of twelve basic science courses for preparing students to practice medicine and to develop a scientific mind, was tested by the Pair Comparison and Equal-Appearing Interval methods. The test groups consisted of medical schools faculty, medical students and physicians. Three groups of basic sciences, according its relevancy for preparing the students for a career as physicians, were identified. General Pathology, Physiology and Pharmacology comprised the group of maximum revelancy; Biology, Biophysics and Biostatistics formed a group of lowest relevancy. The remaider sciences formed a middle group. Few differences were found when we considered faculty the relevancy to the development of the scientific mind. Ministerio de Educación y Ciencia, Instituto Nacional de Ciencias de la Educación (INCIE). Investigación nº 26. Proyecto: "Pertinencia de las enseñanzas básicas de la carrera de Medicina". Entidades participantes: Universidad de Valladolid. Investigador responsable: Joaquín De Juan Herrero.