Log in
Basic and applied research at the University of Cambridge has culminated in a widely-used risk prediction algorithm ("BOADICEA") for familial breast and ovarian cancer. This user-friendly web-based tool predicts the likelihood of carrying mutations in breast and ovarian cancer high-risk genes (BRCA1 and BRCA2), and the risks of developing breast or ovarian cancer. BOADICEA has been adopted by several national bodies including NICE in the UK (2006 until present), the American Cancer Society and the Ontario Breast Screening Program (both since 2011) for identifying women who would benefit from BRCA1/2 mutation screening, intensified breast cancer screening and chemoprevention.
Targeted Projection Pursuit (TPP) — developed at Northumbria University — is a novel method for interactive exploration of high-dimension data sets without loss of information. The TPP method performs better than current dimension-reduction methods since it finds projections that best approximate a target view enhanced by certain prior knowledge about the data. "Valley Care" provides a Telecare service to over 5,000 customers as part of Northumbria Healthcare NHS Foundation Trust, and delivers a core service for vulnerable and elderly people (receiving an estimated 129,000 calls per annum) that allows them to live independently and remain in their homes longer. The service informs a wider UK ageing community as part of the NHS Foundation Trust.
Applying our research enabled the managers of Valley Care to establish the volume, type and frequency of calls, identify users at high risk, and to inform the manufacturers of the equipment how to update the database software. This enabled Valley Care managers and staff to analyse the information quickly in order to plan efficiently the work of call operators and social care workers. Our study also provided knowledge about usage patterns of the technology and valuably identified clients at high risk of falls. This is the first time that mathematical and statistical analysis of data sets of this type has been done in the UK and Europe.
As a result of applying the TPP method to its Call Centre multivariate data, Valley Care has been able to transform the quality and efficiency of its service, while operating within the same budget.
Advanced technologies for data visualisation and data mining, developed in the Unit in collaboration with national and international teams, are widely applied for development of medical services. In particular, a system for canine lymphoma diagnosis and monitoring developed with [text removed for publication] has now been successfully tested using clinical data from several veterinary clinics. The risk maps produced by our technology provide early diagnosis of lymphoma several weeks before the clinical symptoms develop. [text removed for publication] has estimated the treatment test, named [text removed for publication], developed with the Unit to add [text removed for publication] to the value of their business. Institute Curie (Paris), applies this data mapping technique and the software that has been developed jointly with Leicester in clinical projects.
Visual analytics is a powerful method for understanding large and complex datasets that makes information accessible to non-statistically trained users. The Non-linearity and Complexity Research Group (NCRG) developed several fundamental algorithms and brought them to users by developing interactive software tools (e.g. Netlab pattern analysis toolbox in 2002 (more than 40,000 downloads), Data Visualisation and Modelling System (DVMS) in 2012).
Industrial products. These software tools are used by industrial partners (Pfizer, Dstl) in their business activities. The algorithms have been integrated into a commercial tool (p:IGI) used in geochemical analysis for oil and gas exploration with a 60% share of the worldwide market.
Improving business performance. As an enabling technology, visual analytics has played an important role in the data analysis that has led to the development of new products, such as the Body Volume Index, and the enhancement of existing products (Wheelright: automated vehicle tyre pressure measurement).
Impact on practitioners. The software is used to educate and train skilled people internationally in more than 6 different institutions and is also used by finance professionals.
Researchers in Cambridge have developed a data standard for storing and exchanging data between different programs in the field of macromolecular NMR spectroscopy. The standard has been used as the foundation for the development of an open source software suite for NMR data analysis, leading to improved research tools which have been widely adopted by both industrial and academic research groups, who benefit from faster drug development times and lower development costs. The CCPN data standard is an integral part of major European collaborative efforts for NMR software integration, and is being used by the major public databases for protein structures and NMR data, namely Protein Data Bank in Europe (PDBe) and BioMagResBank.
The research of Professor Bentley and colleagues has developed a novel and practical analysis that commercial and government organisations can use to extract social network `signatures' and collective decision patterns from available data that were never intended to measure social influence or `decision fatigue' directly (such as sales figures, trend popularity statistics or other `big data'). Since Bentley's arrival to Bristol University in 2011, this research and impact have proceeded in parallel, as for example in multiple publications with Paul Ormerod, who is co-director of Volterra LLP, an influential consulting company whose direction was informed by this research with Ormerod. Other research was published in trade magazines (e.g., refs [f, g] below) shortly after academic publication (1-6), which led other organisations to follow in applying the methods of data analysis. For example, in 2011-12 Sony Electronics Europe (via Anomaly Communications, London) contracted this analysis of their sales data to distinguish market segments characterised by consumers looking toward `expert' opinion, versus segments in which indiscriminate copying was more prevalent, which fundamentally steered the segment-specific marketing strategies.
The security of data in printing and network environments is an area of increasing concern to individuals, businesses, government organisations and security agencies throughout the world. Mathematical algorithms developed at the School of Mathematics at Cardiff University represent a significant step-change in existing data security techniques. The algorithms enable greater security in automatic document classification and summarisation, information retrieval and image understanding. Hewlett-Packard (HP), the world's leading PC vendor, funded the research underpinning this development and patented the resulting software, with the aim of strengthening its position as the market leader in this sector of the global information technology industry. Hewlett Packard has incorporated the algorithms in a schedule of upgrades to improve the key security features in over ten million of their electronic devices. Accordingly, the impact claimed is mitigating data security risks for HP users and clients and substantial economic gain for the company.
Research carried out at Birkbeck's Department of Computer Science and Information Systems since 2000 has produced techniques for the management and integration of complex, heterogeneous life sciences data not previously possible with large-scale life sciences data repositories. The research has involved members of the department and researchers from the European Bioinformatics Institute (EBI) and University College London (UCL) and has led to the creation of several resources providing information about genes and proteins. These resources include the BioMap data warehouse, which integrated the CATH database — holding a classification of proteins into families according to their structure, the Gene3D database — holding information about protein sequences, and other related information on protein families, structures and the functions of proteins such as enzymes. These resources are heavily utilised by companies worldwide to explore relationships between protein structure and protein function and to aid in drug design.
Small area estimation (SAE) describes the use of Bayesian modelling of survey and administrative data in order to provide estimates of survey responses at a much finer level than is possible from the survey alone. Over the recent past, academic publications have mostly targeted the development of the methodology for SAE using small-scale examples. Only predictions on the basis of realistically sized samples have the potential to impact on governance and our contribution is to fill a niche by delivering such SAEs on a national scale through the use of a scaling method. The impact case study concerns the use of these small area predictions to develop disease-level predictions for some 8,000 GPs in England and so to produce a funding formula for use in primary care that has informed the allocation of billions of pounds of NHS money. The value of the model has been recognised in NHS guidelines. The methodology has begun to have impact in other areas, including the BIS `Skills for Life' survey.