The knowledge network is the brain of precision medicine, with the informatics power to aggregate all types of biological information into an information commons, stratify it into “layers” of distinct data types, and then discern patterns and connections within and between layers. This process builds a network of knowledge from across disciplines. This new knowledge, in turn, can be visualized and made accessible to researchers and health practitioners.
The knowledge network will pull data out of silos, connecting the wealth of information that already exists from basic molecular research, clinical insights, environmental data and others. The connections and patterns that emerge will suggest testable hypotheses and new conceptual syntheses for researchers, implicate mechanisms of disease for researchers and clinicians, and enable more precise diagnoses and treatments for individual patients. And it will continuously acquire new data – from laboratory experiments and clinical trials to electronic health records and pedometer readings—that will inform our collective understanding of health.
As the network broadens and deepens, a clinician sitting with a patient could access information to help make a tailored assessment, drawing from molecular and demographic datasets, accessing results from patients participating in a recent and related study, connecting that with clinical imaging and behavioral information, and comparing the patient across a population of other patients who are both similar and different. Importantly, building the network is a vast and continuous undertaking, but it need not be complete to contribute in powerful ways. Thus, pilot projects, even on a small scale, can have an impact.
The knowledge network also will enable researchers to interact to share new findings, processes and ideas. Those developing the pilot project are carefully considering provenance: a thorough auditing system will track uploads, downloads and further uses of current data. In addition, the efforts of building the UCSF knowledge network are yielding modular computational tools that can be adapted to a variety of needs and data environments, with an eye to future use by researchers and clinicians with a wide range of needs.
- Wynton: The foundation of the UCSF computational infrastructure is a computing cluster called Wynton. Wynton is a large, shared computing cluster underlying UCSF’s Research Computing Capability. Funded and administered cooperatively by UCSF campus IT and key research groups, it is available to all UCSF Faculty for their research, and consists of different profiles suited to various biomedical and health science computing needs.
- Information Commons: is an environment where a diversity of data and tools are made available to investigators for research. UCSF Information Commons provides a shared repository data, tools and models for today’s demands of integrative research and precision medicine. It brings together the worlds of clinical, basic science and population research, at a scale that manages huge sizes of multi-factor and multi-modal data – and keeps its users compliant with regulation.
- SPOKE: An additional facet of UCSF’s computational infrastructure is a common core Knowledge Network, code-named “SPOKE” (Scalable Precision Medicine Oriented Knowledge Engine). This network captures the essential structure of biomedicine and human health, which is “pathways,” or node-arc graphs. A fundamental construct of SPOKE is the “layers” the make up a human: genetics, epigenomics, proteins, tissues, organs, clinical phenotypes, environment and lifestyle. This process builds a network of knowledge from across disciplines and layers. This new knowledge network can, in turn, be visualized and made accessible to researchers and health practitioners so patterns and connections can be discerned within and between layers. SPOKE pulls data out of silos, connecting the wealth of information that already exists from basic molecular research, clinical insights, environmental data and others. Current data includes 19 databases such as LINCS, GWAS Catalog, ChemBL, DrugBank, SIDER and iRefIndex. The connections and patterns that emerge suggest testable hypotheses and new conceptual syntheses for researchers, implicate mechanisms of disease for researchers and clinicians, and enable more precise diagnoses and treatments for individual patients. SPOKE continuously acquires new data – from laboratory experiments and clinical trials to electronic health records and pedometer readings – that when brought together will inform our collective understanding of health.
- KNECT: This pilot of precision medicine at the UCSF Memory and Aging Center is the core technology of the Knowledge Network being developed at UCSF. KNECT is focused on providing new data-driven analyses of brain function in patients with neurodegenerative conditions by combining data gathered on one group of patients by researchers of different disciplines. Via an easy-to-use dashboard, it allows clinicians from different fields to upload and make meaning of diverse datasets. Doctors and patients can then discuss those visualized data analyses together.