Sage Bionetworks to develop data platform to accelerate rare disease diagnosis. Published: 7 September 2026 The average rare disease diagnosis takes six years, with some waiting decades. More than 10,000 rare diseases affect around 30 million Americans, costing the US health system an estimated $1 trillion a year, despite only 5% having an approved treatment. Sage Bionetworks and the Advanced Research Projects Agency for Health (ARPA-H) are partnering to develop the Rare Disease Data Commons (RDDC), a central data repository and benchmarking platform for ARPA-H's new Rare Disease AI/ML for Precision Integrated Diagnostics (RAPID) program. RAPID is awarding Sage Bionetworks up to $28 million over 4.5 years to advance the program's goal to transform rare disease diagnosis, enabling patients to receive answers sooner while reducing costs for families and the health system. ARPA-H's RAPID program is split into four technical areas: * Assembling a large-scale longitudinal clinical dataset from electronic health records. * Collecting patient-reported data and other modalities such as genomics, imaging, video, voice, and wearable data. * Building the platform that ingests data from the first two areas, harmonises it, governs access, and benchmarks the diagnostic AI built on top of it. * Validating tools in clinical settings. Patient experience partners will work across all four areas throughout the program. The primary obstacle the RDDC addresses is the shortage of usable data. Rare disease data sits in separate institutions, coded differently, and governed under terms that must be renegotiated every time a new research group requests access. Robert Allaway, director of Rare Disease at Sage Bionetworks and Principal Investigator (PI) for the project, said, "Rare disease data are locked into islands. A foundation typically funds one disease, collects one data type, and the result doesn't connect to what the next foundation collected for the next disease. We proposed a platform that connects these islands into an archipelago, solving data challenges shared across the rare disease community once instead of ten thousand times." The RDDC will have five connected modules that bring in data from other RAPID teams and outside contributors, make it comparable across sources by building on tools created for the ARPA-H Biomedical Data Fabric Toolbox, and set rules for who can use it and how. Where data cannot be centralised, RDDC supports federated integration and interoperates with other trusted research environments instead of becoming another silo. Milen Nikolov, director of Product Innovation at Sage Bionetworks and co-PI for the project, said, "People living with rare diseases often drive their care odyssey, working closely with clinicians and researchers to gain insight into their diagnosis, connect to a community of others with similar conditions, and understand their prognosis and treatment options. The RDDC modules advance breakthrough technology - translating disjoint, multi-modal datasets into reliable information usable by patients, researchers and clinicians - throughout this odyssey and across all rare diseases." Jineta Banerjee, associate director of Advanced Data Analytics at Sage Bionetworks and co-PI for the project, said, "For rare diseases, where no individual dataset may ever be large enough, our greatest opportunity is to learn across boundaries of individual rare diseases. In the Rare Disease Data Commons, we aim to meaningfully connect datasets from multiple rare diseases to allow advanced computational methods to uncover shared and latent patterns that would help improve diagnosis or identify novel therapeutic opportunities." The most publicly visible deliverable is the Rare Disease Benchmark Dataset: a versioned, accessible resource designed to provide a common, credible basis for evaluation to researchers and developers working on diagnostic AI. Today, groups developing rare disease AI methods often evaluate on proprietary data, making it difficult to compare results or know which methods are worth clinically pursuing. The benchmark would enable standardised evaluation across the field while protecting patient privacy through de-identification, governed access, and appropriate data-use controls. Allaway said, "It's incredibly challenging to make apples-to-apples comparisons between AI tools for rare diseases. A group builds a model, reports on its own cohort, and there is no way to tell whether the next method is better or just due to differences in the test data. We are building a place where the evaluation is the same for everyone." Luca Foschini, president and CEO of Sage Bionetworks, said, "About one in ten Americans lives with a rare disease; many of them are still searching for answers, and they have been waiting a long time for infrastructure built to their scale. Building in the open while protecting privacy is a complex undertaking, but it's the only way we can create trust and share learnings for the benefit of all patients, rare disease and beyond." Sage Bionetwork develops and operates Synapse, one of the NIH-listed generalist repositories, managing more than 4.5 petabytes of biomedical research data. The RDDC builds on 15 years of Sage's experience developing governed data commons for sensitive biomedical data. Sage is building the Rare Disease Data Commons with Cirro Bio, Netrias, the Gyori lab at Northeastern University, the Alsentzer lab at Stanford University and the Undiagnosed Diseases Network Foundation.

Undiagnosed Diseases Network Foundation named Patient Experience Partner for ARPA-H's groundbreaking RAPID program. UDNF to bring the voice and experiences of the undiagnosed community to a federal initiative aimed at transforming rare disease diagnosis. WASHINGTON, DC, UNITED STATES, August 31, 2026 /EINPresswire.com/ - The Undiagnosed Diseases Network Foundation (UDNF) today announced it has been selected as a Patient Experience Partner (PXP) for the Advanced Research Projects Agency for Health's (ARPA-H) Rare Disease AI/ML for Precision Integrated Diagnostics (RAPID) program - a landmark federal initiative designed to dramatically reduce the diagnostic odyssey for millions of Americans living with rare and undiagnosed diseases. RAPID is built on the question of "what if we could end the rare diseases diagnostic odyssey?". As a Patient Experience Partner, UDNF will bring the voice and experiences of the undiagnosed patient and family community to the researchers, technologists, and innovators working to solve the rare disease diagnostic challenge. UDNF will partner with Sage Bionetworks and its collaborators to ensure that the lived experience of patients informs the development and deployment of the Rare Disease Data Commons and RAPID's AI-driven solutions. Artificial intelligence is a powerful tool, but it cannot tell you what it feels like to spend a decade without a diagnosis, or why a family in a rural community can't access the specialist their child needs. UDNF will help bridge that gap, ensuring RAPID's data resources and platform are built not just for patients, but with them. "Serving as a Patient Experience Partner for ARPA-H's RAPID program is a profound recognition that solving the diagnostic odyssey requires more than science - it requires the voice, wisdom, and experience of the patients and families who live it every day," said Dr. Danielle Carnival, CEO, Undiagnosed Diseases Network Foundation. "UDNF exists to ensure that the undiagnosed community is not just the subject of research, but an active force shaping the solutions that will change lives." The Scale of the Problem More than 10,000 unique rare diseases affect over 350 million people worldwide, including one in ten Americans. For people facing a rare disease, the journey to a diagnosis is long, isolating, and costly. Families encounter more barriers than pathways and more questions than answers. Patients may see dozens of doctors and undergo countless tests before finally receiving a diagnosis. The diagnostic odyssey endured by rare disease patients lasts an average of six years but can go on for decades. Each diagnostic odyssey costs an average of $500,000 per patient and rare diseases cost the U.S. economy $1 trillion, annually. For families in the undiagnosed community, these numbers represent years of uncertainty, missed treatments, and an exhausting and expensive search for answers. It is estimated that half of all individuals with a rare disease remain undiagnosed or misdiagnosed, leading to inappropriate care, irreversible disease progression, and extensive medical costs. UDNF's Role as Patient Experience Partner As a RAPID Patient Experience Partner, UDNF will: Amplify patient and family voices to ensure that RAPID's diagnostic tools reflect the real-world needs of the undiagnosed community; Facilitate engagement by connecting RAPID program teams with patients, caregivers, and families who can provide input on research design, tool development, and deployment of new tools; Bridge the gap between cutting-edge AI research and the communities it is designed to serve, ensuring solutions are accessible, equitable, and patient-centered; Advocate for access to rare disease diagnosis, ensuring that underserved and underrepresented communities are included in RAPID's datasets and that tools are designed for use in communities across the United States A Turning Point for the Undiagnosed Community The RAPID program represents a pivotal moment in the fight to end the diagnostic odyssey. By harnessing the power of artificial intelligence and the scale of federal investment, RAPID has the potential to dramatically shorten the time from first symptom to accurate diagnosis - a transformation that could change the trajectory of millions of lives. "For too long, families have navigated the diagnostic journey largely alone - moving from specialist to specialist, repeating the same tests, and waiting years for answers," said Dr. F Sessions Cole, Chair, UDNF Board of Directors. "ARPA-H's RAPID program, with patient experience at its core, represents the kind of bold, innovative thinking that is needed to make progress toward UDNF's mission to end the diagnostic odyssey." UDNF's selection as a Patient Experience Partner builds on the organization's longstanding commitment to ensuring that patient and family voices shape the research, policy, and systems that govern rare disease diagnosis and care. Danielle Carnival Undiagnosed Diseases Network Foundation +1 202-240-2452 [email protected] Legal Disclaimer: EIN Presswire provides this news content "as is" without warranty of any kind. National Capital Daily do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.

Sage Bionetworks will build the data platform for a new federal program to accelerate rare disease diagnosis. The Rare Disease Data Commons will unify fragmented patient data, establish privacy-protected benchmark datasets, and run community AI challenges Rare disease data are locked into islands. A foundation typically funds one disease, collects one data type, and the result doesn't connect to what the next foundation collected for the next disease." - Robert Allaway, PhD SEATTLE, WA, UNITED STATES, August 31, 2026 / EINPresswire.com / - The average rare disease diagnosis takes six years. For some families it takes decades. More than 10,000 rare diseases affect roughly 30 million Americans, cost the U.S. health system an estimated $1 trillion a year, and yet only about 5 percent have an approved treatment. Sage Bionetworks is partnering with the Advanced Research Projects Agency for Health (ARPA-H) to build the Rare Disease Data Commons (RDDC), the central data repository and benchmarking platform for ARPA-H's new Rare Disease AI/ML for Precision Integrated Diagnostics (RAPID) program. RAPID is awarding Sage Bionetworks up to $28 million over 4.5 years to advance the program's mission to transform rare disease diagnosis - helping patients get answers sooner while reducing costs for families and the health system. ARPA-H's RAPID program is organized into four technical areas. The first focuses on assembling a large-scale longitudinal clinical dataset from electronic health records. The second technical area will collect patient-reported data and other modalities: genomics, imaging, video, voice, and wearable data. Sage's team leads the third technical area, building the platform that takes data in from both, harmonizes it, governs access, and benchmarks the diagnostic AI built on top of it. A fourth area, will validate tools in clinical settings. Patient experience partners work across all four areas throughout the program. The primary obstacle the RDDC addresses is not a shortage of AI methods, rather, it is a shortage of usable data. Rare disease data sits in separate institutions, coded differently, and governed under terms that have to be renegotiated every time a new research group wants to work with it. "Rare disease data are locked into islands. A foundation typically funds one disease, collects one data type, and the result doesn't connect to what the next foundation collected for the next disease." says Robert Allaway, PhD, Director of Rare Disease at Sage Bionetworks and Principal Investigator (PI) for the project. "We proposed a platform that connects these islands into an archipelago, solving data challenges shared across the rare disease community once instead of ten thousand times." The RDDC will contain five connected modules that bring in data from the other RAPID teams and from outside contributors, make it consistent enough to compare across sources - building on tools created for the ARPA-H Biomedical Data Fabric Toolbox - and set clear rules for who can use it and how. Where data cannot be centralized, RDDC supports federated integration and interoperates with other trusted research environments rather than becoming another silo. "People living with rare diseases often drive their care odyssey, working closely with clinicians and researchers to gain insight into their diagnosis, connect to a community of others with similar conditions, and understand their prognosis and treatment options." says Milen Nikolov, PhD, Director of Product Innovation at Sage Bionetworks and co-PI for the project. "The RDDC modules advance breakthrough technology - translating disjoint, multi-modal datasets into reliable information usable by patients, researchers and clinicians - throughout this odyssey and across all rare diseases." "For rare diseases, where no individual dataset may ever be large enough, our greatest opportunity is to learn across boundaries of individual rare diseases. In the Rare Disease Data Commons, we aim to meaningfully connect datasets from multiple rare diseases to allow advanced computational methods to uncover shared and latent patterns that would help improve diagnosis or identify novel therapeutic opportunities." says Jineta Banerjee, PhD, Associate Director of Advanced Data Analytics at Sage Bionetworks and co-PI for the project. The most publicly visible deliverable is the Rare Disease Benchmark Dataset: a versioned, broadly accessible resource designed to give researchers and developers working on diagnostic AI a common, credible basis for evaluation. Today, groups developing rare disease AI methods often evaluate on proprietary data, making it difficult to compare results or know which methods are worth pursuing clinically. The benchmark would enable standardized evaluation across the field while maintaining strong protections for patient privacy through de-identification, governed access, and appropriate data-use controls. "It's incredibly challenging to make apples-to-apples comparisons between AI tools for rare diseases," says Allaway. "A group builds a model, reports on its own cohort, and there is no way to tell whether the next method is better or just due to differences in the test data. We are building a place where the evaluation is the same for everyone." "About one in ten Americans lives with a rare disease, many of them are still searching for answers, and they have been waiting a long time for infrastructure built to their scale," says Luca Foschini, PhD, President & CEO of Sage Bionetworks. "Building in the open while protecting privacy is a complex undertaking, but it's the only way we can create trust and share learnings for the benefit of all patients, rare disease and beyond." Sage Bionetwork develops and operates Synapse, one of the NIH-listed generalist repositories, managing more than 4.5 petabytes of biomedical research data. The RDDC builds on 15 years of Sage's experience developing governed data commons for sensitive biomedical data. Sage is building the Rare Disease Data Commons with Cirro Bio (https://cirro.bio/), Netrias (https://www.netrias.com/), the Gyori lab at Northeastern University (https://gyorilab.github.io/), the Alsentzer lab at Stanford University (https://alsentzerlab.org/) and the Undiagnosed Diseases Network Foundation (https://udnf.org/). Organizations interested in contributing data or testing methods on the platform can contact the Sage Bionetworks team at [email protected]. About Sage Bionetworks Sage Bionetworks is a Seattle-based 501(c)(3) nonprofit research organization founded in 2009 to drive a new age of discovery through open science and radical collaboration. Sage develops and operates Synapse, an NIH-listed generalist data repository, and has spent 15 years building governed data commons for sensitive biomedical data on behalf of the research community. Learn more at https://sagebionetworks.org. Funding acknowledgment This research was, in part, funded by the Advanced Research Projects Agency for Health (ARPA-H). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States Government. Robert Allaway Sage Bionetworks +1 206-928-8250 [email protected] Legal Disclaimer: EIN Presswire provides this news content "as is" without warranty of any kind. The MarCom Journal do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.