Empirical approach to the analysis of university student absenteeism. Proposal of a questionnaire for students to evaluate the possible causes

Works on student absenteeism in the universities have not been preferential for the authors in the field of educational research. Usually, what has been made is an approach to the available absenteeism data as an intervening variable or as a variable characteristic of the educational process, but not as a dependent variable in the strict sense of the term. In this work, we intend to make an empirical approach to the possible reasons of student absenteeism. There is a double point of view: the students" and the professors"; the reasons that justify it according to its protagonists are studied. This paper focuses on the six university degrees taught at the School of Economy and Business of the University of Barcelona (Facultat d"Economia i Empresa de la Universitat de Barcelona). An"ad-hoc" questionnaire has been prepared and the opinions of 1,162 undergraduates have been analyzed. The reasons given by each population differ in hierarchy and motivations

The implicitome: A resource for rationalizing gene-disease associations

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing

The Implicitome: A Resource for Rationalizing Gene-Disease Associations

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations.UB – Publicatie