Elsevier and UCL Collaborate to Predict Functionality of Novel Proteins to Aid the Discovery of New Drug Targets

LONDON, October 19, 2015 /PRNewswire/ --

UCL computer science protein modelling team to use Pathway Studio® data to develop new prediction algorithms  

Elsevier, a world-leading provider of scientific, technical and medical information products and services, today announced that it is collaborating with University College London (UCL) to analyze 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 analyze and visualize 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 Professor 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, Product Development, Elsevier R&D Solutions. "Many researchers find it challenging to prioritize 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."

Pathway Studio helps biomedical researchers to understand complex biological processes, like those responsible for disease progression and responsiveness to treatment. It provides a comprehensive resource of easily searchable molecular cell interactions and tools for the analysis and visualization of disease mechanisms, gene expression, and proteomics and metabolomics data; saving researchers time and improving their chances of finding novel results. Pathway Studio enables biological researchers to import and analyze their experimental data in the context of the scientific literature, giving greater insight into the mechanisms of disease and accelerating biological research.

About Elsevier   

Elsevier is a world-leading provider of information solutions that enhance the performance of science, health, and technology professionals, empowering them to make better decisions, deliver better care, and sometimes make groundbreaking discoveries that advance the boundaries of knowledge and human progress. Elsevier provides web-based, digital solutions - among them ScienceDirect, Scopus, Elsevier Research Intelligence and ClinicalKey - and publishes more than 2,500 journals, including The Lancet and Cell, and more than 33,000 book titles, including a number of iconic reference works. Elsevier is part of RELX Group plc, a world-leading provider of information solutions for professional customers across industries. http://www.elsevier.com

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