Li Ka Shing Foundation gives $3 million to Stanford for 'big data' initiative

Jan. 24, 2014

The Li Ka Shing Foundation has provided a $3 million planning grant to the Stanford University School of Medicine for a new initiative to explore ways of harnessing the vast repositories of biomedical data to improve human health and lower health-care costs. This latest gift will further a collaborative effort between Stanford and the University of Oxford in England.

“Big data” is considered the next revolution in biomedical science, speeding up discovery of new drugs and offering patients better, more-personalized treatments while helping to reduce costs.

“In the world of medicine, we have a tsunami of data crashing over us, including electronic patient records, DNA sequencing, biological data on disease mechanisms, clinical trials, medical imaging and pharmaceutical records. We can put all these large data sets to work to identify innovative approaches to treatment and to improving access to care,” said Lloyd Minor, MD, dean of the School of Medicine. “We are extremely grateful to Mr. Li Ka-shing for helping to bring us closer to this goal.”

A longtime supporter of Stanford University, Li Ka-shing is a self-made businessman and philanthropist who manages a diversity of industrial interests through his Hong Kong-based companies, Cheung Kong Holdings and Hutchison Whampoa. He was the principal benefactor of the medical school’s core education building, the 3-year-old Li Ka Shing Center for Learning and Knowledge. Through his foundation, he has provided more than $37 million to the School of Medicine for a wide range of projects, including work in translational medicine, fellowship training, an endowed professorship and vitally needed equipment. The foundation has contributed more than $1.85 billion to projects worldwide, with a focus on innovations in education and medical care.

Mr. Li said, “We stand on the precipice of realizing the promise of big data in transforming the future of biomedical sciences and I am very excited that our foundation can enable these two eminent institutions to join forces and bring us to the next level of discovery that will revolutionize patient care and treatments to solve today’s health-related challenges.

The amount of data being generated now each year is in the zettabyte range — the number 1 followed by 21 zeroes. With accelerated data-processing and data-transmitting abilities, physician-scientists can capitalize on this information to improve diagnostic and treatment capabilities and gain new insights into who develops a disease and why. But it is an enormous challenge to generate useful knowledge from all this vast and complex data, requiring large-scale collaborations between individuals and institutions.

Through the initiative, the two institutions will work together to solve large-number problems at a global scale to improve health worldwide. Together, the universities can accelerate discovery from large-number data sets to provide new insight into disease and to apply targeted therapies on an unprecedented scale.

The University of Oxford faculty members are leaders in one of the largest patient databanks in the world, the UK biobank, which has biomedical information on some 500,000 individuals. Stanford, with excellence in computer science, engineering, statistics, genetics and bioinformatics, as well as with extensive ties to Silicon Valley, excels in technical innovation and data management and analysis.

By assembling data on large populations, scientists can discern patterns that would not otherwise be apparent by studying an individual’s genes. For instance, they can determine whether a particular genetic variant is significant or just an artifact. And they can better determine what drugs are likely to have an impact on the activities of a particular gene.

Based on such findings, scientists aim to develop new medications and low-cost therapies. They also plan to develop a real-time mobile application with the ability to collect biometric and other health data, such as heart rate and blood pressure for patients with cardiovascular disease, to actively monitor patients recovering from surgery or to prevent an adverse health event from occurring. The scientists ultimately will combine this data with other health metrics, such as genomic-sequencing data and electronic medical records, and use bioinformatics analysis to predict which patients are at higher risk for certain diseases and which ones could benefit from earlier intervention.

The two universities hope to eventually recruit new faculty working on big data, including scientists from the fields of genetics, epidemiology, public health, clinical medicine, computer science and information technology, statistics and bioinformatics.

As big data is an emerging field, there is a pressing need to train new scientists to carry on the work. A scholars program will train PhD students and postdoctoral scholars who will work with faculty at both institutions.

The initial grant also includes funds for a major conference on big data in May 2014 at Stanford, a follow-up to the “Big Data in Biomedicine” conference hosted by the School of Medicine in May 2013. Some 300 individuals attended the three-day gathering, which included a distinguished group of presenters from both Oxford and Stanford, among other institutions. More than 2,700 logged in to the live stream on the web.

The Stanford arm of the effort will be directed by Euan Ashley, MD, PhD, associate professor of cardiovascular medicine and director of the Stanford Center for Inherited Cardiovascular Disease.  Ashley, who received his PhD from the University of Oxford, focuses on developing methods for interpreting genome-sequencing data to improve diagnosis of genetic disease and to develop targeted therapies for patients.

The planning grant from the Li Ka Shing Foundation is the first philanthropic commitment to the School of Medicine’s big data effort, a major initiative of the Campaign for Stanford Medicine, which aims to improve patient care in the United States and around the world. 

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