Sage Bionetworks

Sage Bionetworks is a non-profit research organization that seeks to develop predictors of disease and accelerate health research through the creation of open systems, incentives, and standards. They create strategies and platforms that empower researchers to share and interpret data on a colossal scale, crowdsource tests for new hypotheses, and contribute to knowledge  through community challenges. At Sage Bionetworks they believe that this advance will be best harnessed when individuals and groups can collaborate openly on discoveries, with a fundamental shift in the traditional roles and rewards for individuals and organizations involved.  They work to redefine how complex biological data is gathered, shared and used, redefining it through open systems, incentives, and norms. They challenge the traditional roles of individuals and groups, patients and researchers. Their work includes the building of platforms and services and undertaking research developing predictors relating to health. They collaborate with a network of partners and individuals, to redefine the way biomedical research results in better health. Their work revolves around bringing this mission and vision to reality, underpinned by the belief that being open is critical. As part of living their philosophy, all of their tools, platforms, and products are open source. Their software is available in Github, and their non-software creative works are licensed under the Creative Commons Attribution 3.0 Unported license except for legacy publications in closed journals. They now dedicate funds to pay author processing charges for full Open Access on research publications and are actively working to free up their backfile of closed publications. You can also view their most recent organizational goals or view a historical presentation from 2009 in which Sage was first proposed inside Merck as an internally hosted open source project. The five areas they believe now are present and will enable significant advance in this field are: 1 – It is now possible to readily generate massive amount of human “omic’s” data as a commodity purchase 2 – Network modelling approaches for diseases are emerging 3 – IT Infrastructure and Cloud compute capacity allows a generative open approach to biomedical problem solving 4 – Nascent movement for patients to control sensitive information allowing sharing 5 – Open social media allows citizens and experts to use collaborative tactics to solve problems Combining these factors would suggest that it is only a matter of time, before significant system breakthroughs are made in biomedical research, in a number of fields, particularly oncology. In their dreams, you may be able to predict your risk of developing a certain cancer through genetic and other information, identify the genetic and lifestyles changes responsible and target those specifically to prevent or treat that condition. The reality is that they are a long way from this dream – and they believe they all need to start thinking about things differently to be able to use the opportunity. They need to start looking at these complex problems as complex problems. Although advances are made at a theoretical or laboratory level, they often fail when replicated in an animal or person. At the heart of this is the undisputable fact that the complexity of biological systems mean that solving one small part of the problem may work alone but not in the wider context of the system. They need to reassess incentives for researchers and institutions to encourage truly collaborative research. There are also challenges inherent in the research system. Researchers are recognized for being the first to make significant discoveries, and obtaining future funding by the individual or the organization is inherently linked to this status. This inhibits the sharing of data or discoveries between research centers. Although collaborations exist they are generally limited to a small number of like institutions. They need to address the ability of publications to truly report results of research in a meaningful and interrogable way. Publications often report results without the ability of readers to access the models behind the research, which in this field is critical to understanding and assessing the advance. They need to reassess the mechanisms of data sharing and collection to allow compatibility and collaboration between institutions, and actively encourage this to happen. Where data is the currency of this new system, it is frequently not able to be accessed by the broadest range of researchers either due to access practicalities or often due to a lack of consent by the patient for broader use. They need to focus funding to rewarding for results rather than work. Funding is often given to just one institution or group of researchers, based on reputation. This both misses the vast array of talent available and payment is given for work rather than a concrete result. They need to maximize the input of patients in research. Patients are the group that research can ultimately affect most significantly. Yet, they often are asked purely for defined information or participation in a research project. Research is often initiated from top down, defined by the research community, rather from bottom up. In this way, they can miss input from these disease or patient communities in defining the truly relevant questions to them, as well as missing out on a wealth of information that they could prospectively provide.