Showing papers presented at "Semantic Web Applications and Tools for Life Sciences in 2016"

Registries of domain-relevant semantic reference models help bootstrap interoperability in domains with fragmented data resources

Abstract: The specialist field of rare diseases must connect its vast array of globally distributed disease and patient registries to maximise their value. Unfortunately, many registries are “boutique”, with few or no staff with formal informatics training. At a series of Bring Your Own Data workshops, we helped registry owners transform their data into formally structured triple stores following the Linked Data principles and demonstrated the potential of data linkage. We documented several useful approaches that we believe could be followed independently by other registry owners worldwide, including: that the transformation to Linked Data could be considered as passing through layers of increasing semantic complexity; that only a subset of ontologies are relevant at each layer; and that certain data transformation processes could be modelled as an “archetype”, and presented to registry staff to fill-in with their data. We propose that formally capturing these ontological layers and archetypes, and registering them as a reference and teaching resource will facilitate the wider community of non-expert data owners self-direct their own data transformations.

AgroLD indexing tools with ontological annotations

Abstract: The Agronomic Linked Data project (AgroLD) is a Semantic Web knowledge base designed to integrate data from various publicly available plant centric data sources. The aim of AgroLD project is to provide a portal for bioinformaticians and domain experts to exploit the homogenized data towards enabling to bridge the knowledge. Here we present new tools that enable " full text search " functionalities with Elastic clusters and enhance data annotation with ontologies.

MuEVo, a breast cancer Consumer Health Vocabulary built out of web forums

Abstract: Semantically analyze patient-generated text from a biomedical perspective is challenging because of the vocabulary gap between patients and health professionals. The medical expertise and vocabulary is well formalized in standards terminologies and ontologies, which enable semantic analysis of expert-generated text; however resources which formalize the vocabulary of health consumers (patients and their family, laypersons in general) remain scarce. The situation is even worse if one is interested in another language than English. In previous studies, we attempted to produce a French preliminary Consumer Health Vocabulary (CHV) by mining the language used within online public forums & Facebook groups about breast cancer. In this work, we show our effort to concretely align the vocabulary produced to standard terminologies and to represent its content (terms & mappings) using semantic web languages such as RDF and SKOS. We used a sample of 173 relations built around 64 expert concepts which have been automatically (89%) or manually (11%) aligned to standard biomedical terminologies, in our case: MeSH, MedDRA and SNOMEDint. The resulting vocabulary, called MuEVo (Multi-Expertise Vocabulary) and the mappings are publicly available in the SIFR BioPortal French biomedical ontology repository.

Studying the Cohesion Evolution of Genes Related to Chronic Lymphocytic Leukemia Using Semantic Similarity in Gene Ontology and Self-Organizing Maps

Abstract: A significant body of work on biomedical text mining is aimed at uncovering meaningful associations between biological entities, including genes. This has the potential to offer new insights for re ...