A case study: semantic integration of gene-disease associations for type 2 diabetes mellitus from literature and biomedical data resources.

In the Semantic Enrichment of the Scientific Literature (SESL) project, researchers from academia and from life science and publishing companies collaborated in a pre-competitive way to integrate and share information for type 2 diabetes mellitus (T2DM) in adults. This case study exposes benefits from semantic interoperability after integrating the scientific literature with biomedical data resources, such as UniProt Knowledgebase (UniProtKB) and the Gene Expression Atlas (GXA). We annotated scientific documents in a standardized way, by applying public terminological resources for diseases and proteins, and other text-mining approaches.

Towards virtual knowledge broker services for semantic integration of life science literature and data sources.

Research in the life sciences requires ready access to primary data, derived information and relevant knowledge from a multitude of sources. Integration and interoperability of such resources are crucial for sharing content across research domains relevant to the life sciences. In this article we present a perspective review of data integration with emphasis on a semantics driven approach to data integration that pushes content into a shared infrastructure, reduces data redundancy and clarifies any inconsistencies.

Which species is it? Species-driven gene name disambiguation using random walks over a mixture of adjacency matrices.

Motivation: The scientific literature contains a wealth of information about biological systems. Manual curation lacks the scalability to extract this information due to the ever-increasing numbers of papers being published. The development and application of text mining technologies has been proposed as a way of dealing with this problem.

Empowering industrial research with shared biomedical vocabularies.

The life science industries (including pharmaceuticals, agrochemicals and consumer goods) are exploring new business models for research and development that focus on external partnerships. In parallel, there is a desire to make better use of data obtained from sources such as human clinical samples to inform and support early research programmes. Success in both areas depends upon the successful integration of heterogeneous data from multiple providers and scientific domains, something that is already a major challenge within the industry.

What the papers say: text mining for genomics and systems biology.

Keeping up with the rapidly growing literature has become virtually impossible for most scientists. This can have dire consequences. First, we may waste research time and resources on reinventing the wheel simply because we can no longer maintain a reliable grasp on the published literature.

A genomewide association study of skin pigmentation in a South Asian population.

We have conducted a multistage genomewide association study, using 1,620,742 single-nucleotide polymorphisms to systematically investigate the genetic factors influencing intrinsic skin pigmentation in a population of South Asian descent. Polymorphisms in three genes--SLC24A5, TYR, and SLC45A2--yielded highly significant replicated associations with skin-reflectance measurements, an indirect measure of melanin content in the skin. The associations detected in these three genes, in an additive manner, collectively account for a large fraction of the natural variation of skin pigmentation in a South Asian population.