Miguel Angel Mayer

Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

Background Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. Principal Findings We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. Conclusions For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. Availability The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.

SeDeLo: Using Semantics and Description Logics to Support Aided Clinical Diagnosis

Automated medical diagnosis systems based on knowledge-oriented descriptions have gained momentum with the emergence of semantic descriptions. The objective of this paper is to propose a normalized design that solves some of the problems which have been detected by authors in previous tools. The authors bring together two different technologies to develop a new clinical decision support system: description logics aimed at developing inference systems to improve decision support for the prevention, treatment and management of illness and semantic technologies. Because of its new design, the system is capable of obtaining improved diagnostics compared with previous efforts. However, this evaluation is more focused in the computational performance, giving as result that description logics is a good solution with small data sets. In this paper, we provide a well-structured ontology for automated diagnosis in the medical field and a three-fold formalization based on Description Logics with the use of Semantic Web technologies.

Analysis of a Multilevel Diagnosis Decision Support System and Its Implications: A Case Study

Medical diagnosis can be performed in an automatic way with the use of computer-based systems or algorithms. Such systems are usually called diagnostic decision support systems (DDSSs) or medical diagnosis systems (MDSs). An evaluation of the performance of a DDSS called ML-DDSS has been performed in this paper. The methodology is based on clinical case resolution performed by physicians which is then used to evaluate the behavior of ML-DDSS. This methodology allows the calculation of values for several well-known metrics such as precision, recall, accuracy, specificity, and Matthews correlation coefficient (MCC). Analysis of the behavior of ML-DDSS reveals interesting results about the behavior of the system and of the physicians who took part in the evaluation process. Global results show how the ML-DDSS system would have significant utility if used in medical practice. The MCC metric reveals an improvement of about 30% in comparison with the experts, and with respect to sensitivity the system returns better results than the experts.

Applying Semantic Web technologies to improve the retrieval, credibility and use of health-related web resources.

The number of health-related websites is increasing day-by-day; however, their quality is variable and difficult to assess. Various “trust marks” and filtering portals have been created in order to assist consumers in retrieving quality medical information. Consumers are using search engines as the main tool to get health information; however, the major problem is that the meaning of the web content is not machine-readable in the sense that computers cannot understand words and sentences as humans can. In addition, trust marks are invisible to search engines, thus limiting their usefulness in practice. During the last five years there have been different attempts to use Semantic Web tools to label health-related web resources to help internet users identify trustworthy resources. This paper discusses how Semantic Web technologies can be applied in practice to generate machine-readable labels and display their content, as well as to empower end-users by providing them with the infrastructure for expressing and sharing their opinions on the quality of health-related web resources.

Exploring brand-name drug mentions on Twitter for pharmacovigilance.

Twitter has been proposed by several studies as a means to track public health trends such as influenza and Ebola outbreaks by analyzing user messages in order to measure different population features and interests. In this work we analyze the number and features of mentions on Twitter of drug brand names in order to explore the potential usefulness of the automated detection of drug side effects and drug-drug interactions on social media platforms such as Twitter. This information can be used for the development of predictive models for drug toxicity, drug-drug interactions or drug resistance. Taking into account the large number of drug brand mentions that we found on Twitter, it is promising as a tool for the detection, understanding and monitoring the way people manage prescribed drugs.

Using experts feedback in clinical case resolution and arbitration as accuracy diagnosis methodology

This paper proposes a new methodology for assessing the efficiency of medical diagnostic systems and clinical decision support systems by using the feedback/opinions of medical experts. The methodology behind this work is based on a comparison between the expert feedback that has helped solve different clinical cases and the expert system that has evaluated these same cases. Once the results are returned, an arbitration process is carried out in order to ensure the correctness of the results provided by both methods. Once this process has been completed, the results are analyzed using Precision, Recall, Accuracy, Specificity and Matthews Correlation Coefficient (MCC) (PRAS-M) metrics. When the methodology is applied, the results obtained from a real diagnostic system allow researchers to establish the accuracy of the system based on objective facts. The methodology returns enough information to analyze the systems behavior for each disease in the knowledge base or across the entire knowledge base. It also returns data on the efficiency of the different assessors involved in the evaluation process, analyzing their behavior in the diagnostic process. The proposed work facilitates the evaluation of medical diagnostic systems, having a reliable process based on objective facts. The methodology presented in this research makes it possible to identify the main characteristics that define a medical diagnostic system and their values, allowing for system improvement. A good example of the results provided by the application of the methodology is shown in this paper. A diagnosis system was evaluated by means of this methodology, yielding positive results (statistically significant) when comparing the system with the assessors that participated in the evaluation process of the system through metrics such as recall (+27.54%) and MCC (+32.19%). These results demonstrate the real applicability of the methodology used.

Concepto y aplicaciones de la Web 3.0: una introducción para médicos

The development of the Internet is continuous and appears to be never-ending, although with the arrival of Web 3.0 it could be said that the Internet is what its creators intended it to be from the first moment, an extraordinary and immense organised, understandable, and easy to access data base, characteristics still not achieved. The innovations and services included in Web 3.0 will result, in the first place, in better, faster and safer access to quality information. In the second place it should provide better personalisation of the health services that Internet users access, avoiding irrelevant information that may contain wrong, false and dangerous recommendations. However, these changes will have to be accompanied by the legal requirements common to the information society, by the ethical aspects associated with medical care, guaranteeing and contributing, in all cases, to improving the doctor-patient relationship.

Grupos sobre alimentación saludable en Facebook: características y contenidos

OBJECTIVE To determine the features and use of groups related to healthy eating on Facebook. METHOD We carried out a cross-sectional study through the Internet. Using the API on Facebook, we included open groups related to healthy eating in the Spanish language. The variables studied were name, description, category, the number and gender of users, date of creation, number of posts, content of the first 20 posts, and the most recent update. RESULTS We selected 281 open groups for inclusion in the study. Of these, 125 were excluded because the content was unrelated to healthy eating. Finally 156 groups were studied with 14,619 users (10,373 women [71%] and 3,919 men [26.8%]). Dietary products were promoted by 40% of the groups. CONCLUSIONS Facebook is used as a means of communication and for sharing health information. Because many of these groups promote dietary products, their usefulness for health education is doubtful. Health organizations should participate in social media.

Reuse of EHRs to Support Clinical Research in a Hospital of Reference.

Most hospitals have already implemented information systems and Electronic Health Records (EHRs), but the reuse of such data for research is still infrequent. We present a pilot project on the exploitation of clinical information from a Spanish hospital database in the context of the European Medical Information Framework project (EMIF). Specific use cases such as patients with diabetes mellitus type 2, obesity and dementia were assessed, by exploiting EHR data integrated from several separated clinical databases. The possibility to analyse the features of specific groups of patients based on their diagnosis codes can provide new data about relationships between different conditions that can contribute for decision-making, healthcare and research.

Identifying Cases of Type 2 Diabetes in Heterogeneous Data Sources: Strategy from the EMIF Project.

Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93–100%), while drug-based components were the main contributors in RLDs (81–100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies.