Publication: DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

DisGeNET is a comprehensive discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET contains over 380 000 associations between >16 000 genes and 13 000 diseases, which makes it one of the largest repositories currently available of its kind. DisGeNET integrates expert-curated databases with text-mined data, covers information on Read more about Publication: DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes[…]

Publication: Publishing DisGeNET as Nanopublications

The increasing and unprecedented publication rate in the biomedical field is a major bottleneck for discovery in Life Sciences. Although the scientific community is limited an inability to manually curate facts from published papers, recent approaches enable the automatic, scalable and reliable extraction of assertions from the scientific literature. While the publication of assertions on Read more about Publication: Publishing DisGeNET as Nanopublications[…]

Publication: Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research

Background Current biomedical research needs to leverage and exploit the large amount of information reported in scientific publications. Automated text mining approaches, in particular those aimed at finding relationships between entities, are key for identification of actionable knowledge from free text repositories. We present the BeFree system aimed at identifying relationships between biomedical entities with Read more about Publication: Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research[…]

Conference: 6th International Symposium on Semantic Mining in Biomedicine

6th International Symposium on Semantic Mining in Biomedicine (SMBM), Aveiro, Portugal Laura I. Furlong, Alex Bravo, Janet Piñero, Núria Queralt-Rosinach and Michael Rautschka (Parc de Salut Mar Barcelona) presented BeFree: a text mining system to extract relations between genes, diseases and drugs for translational researc

Publication: A Knowledge-Driven Approach to Extract Disease-Related Biomarkers from the Literature

The biomedical literature represents a rich source of biomarker information. However, both the size of literature databases and their lack of standardization hamper the automatic exploitation of the information contained in these resources. Text mining approaches have proven to be useful for the exploitation of information contained in the scientific publications. Here, we show that Read more about Publication: A Knowledge-Driven Approach to Extract Disease-Related Biomarkers from the Literature[…]

Publication: Improving data and knowledge management to better integrate health care and research

Once upon a time, several engineers, biologists and clinicians realized that a lot of information in biomedicine was partitioned into ‘silos’ that do not intercommunicate. These silos were a side effect of the existence of different disciplines required to, for example, develop new drugs. The engineers decided to dispose of the silos, and to put the Read more about Publication: Improving data and knowledge management to better integrate health care and research[…]

Publication: Human diseases through the lens of network biology

One of the challenges raised by next generation sequencing (NGS) is the identification of clinically relevant mutations among all the genetic variation found in an individual. Network biology has emerged as an integrative and systems-level approach for the interpretation of genome data in the context of health and disease. Network biology can provide insightful models Read more about Publication: Human diseases through the lens of network biology[…]