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Elsevier works with UCL to analyse protein function predictions

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Elsevier is to collaborate with University College London (UCL) to analyse the applicability, scope and scientific viability, and value of protein function predictions. UCL will use Pathway Studio, part of Elsevier's R&D Solutions for Pharma & Life Sciences, to analyse and visualise biological relationships.

'Our goal in recent years has been to develop better and better computational methods to predict protein function directly from protein or gene sequences, which could, if successful, ultimately help provide greater insight into the mechanisms of disease,' said David Jones, UCL department of computer science, bioinformatics group.

'Pathway Studio's capabilities in the analysis and interpretation of experimental data will enable us to improve our existing algorithms and uncover valuable new insights hidden in the literature.'

The project is part of the Elsevier-sponsored UCL Big Data Institute, an initiative that explores innovative ways to better serve the needs of researchers through the exploration of new technologies and analytics, as applied to scholarly content and data.

The researchers will compare predictions of protein functions to information extracted from literature in the context of drug discovery; functional prediction for unknown proteins is also aligned with the development of next-generation sequencing.

'This collaboration with UCL is a great example of how Elsevier works closely with its academic partners to support advanced research that adds real value to both parties,' said Jaqui Hodgkinson, vice president for product development at Elsevier R&D Solutions.

'Many researchers find it challenging to prioritise potentially promising drug targets - and prediction models help to focus their research and save them valuable time. Functional predictions for targets, such as interaction partners, biological functions and drugability, are crucial for drug discovery and disease modelling. This approach is particularly relevant when it comes to complex multi-factoral and rare diseases.'