Funders › WA › Sage Bionetworks › 2020
Grants paid by Sage Bionetworks, tax year 2020
EIN 26-4489946 · Seattle, WA · Form 990, Schedule I · NTEE U50
In tax year 2020, Sage Bionetworks (EIN 26-4489946) reported 8 grants paid totaling $1,462,461. Dataset version 2026.09.0, built 2026-09-03.
Every grant, 2020
| Tax year | Recipient | Match | Amount | Type | Purpose | Source filing |
|---|---|---|---|---|---|---|
| 2020 | Institute for Systems Biology Seattle, WA | A | $436,242 | paid | A data and analytic platform for immuno-oncology community. | 202113139349303681 |
| 2020 | Icahn School of Medicine at Mount Sinai New York, NY | A | $317,805 | paid | Interdisciplinary Research to Understand the Complex Biology of Resilience to Alzheimer's Disease Risk is to generate deeper understanding of the mechanisms by which gene environment interactions lead to cognitively resilient phenotypes in the presence of high risk for disease and identify new therapeutic targets amenable to pharmacologic and non-pharmacologic disease prevention strategies. | 202113139349303681 |
| 2020 | University of Washington Seattle, WA | A | $214,468 | paid | Wellcome is engaging the Supplier as a learning partner to prototype and test best ways to build a Global Mental Health Databank (GMH Databank) that holds rich longitudinal data at scale from different global locations on approaches, treatments and interventions potentially relevant to anxiety or depression in 14- 24 year olds in order to help answer the question: "what works for whom and why in relation to prevention, treatment, stopping relapse or managing ongoing difficulties for anxiety or depression including at least some 14-24 year olds" (the "Global Mental Health Databank" or "GMH Databank"). | 202113139349303681 |
| 2020 | Boston University New York, NY | A | $186,548 | paid | Accelerating alzheimer's disease innovation and discovery by lowering barriers to Framingham cognitive aging and dementia data. | 202113139349303681 |
| 2020 | University of Arizona Tucson, AZ | A | $142,840 | paid | The goal of the project is to validate small molecules (from in silico docking on the CD44 binding pocket and peptidomimetics of CD44 peptide) and peptides (from the peptide array) to disrupt the interaction between CD44 and FERM domain-containing proteins. These tools will further test the druggability of CD44 interactions with Moesin, and whether targeting protein-protein interactions can have a more specific effects than global inhibition of the ectodomain of CD44 using antibodies or small molecules. This will pave the way to validating therapeutic hypotheses for AMP-AD targets relating to protein-protein interactions. | 202113139349303681 |
| 2020 | Oregon Health & Science University Portland, OR | A | $75,086 | paid | Existing computational methods for single-cell RNA-seq signal correction and single-cell ATAC-seq peak quantification will be evaluated to select baseline methods respectively for each task, for comparison in the competition. An evaluation criteria will be developed for universal assessment of the participating computational approaches using public and in-house data. Additional experimental data for further validation will be generated and used, including dual experimental assay of scRNA-seq and scATAC-seq. Final results of the community challenge will be disseminated in multiple ways, including scientific manuscript. | 202113139349303681 |
| 2020 | Tufts University Boston, MA | A | $53,548 | paid | Dr. Williams will oversee the project and be responsible for the acquisition, mapping, and transfer of Tuft MC data to N3C, and for its use in a proof-of-concept analysis. He will work with Dr Justin Guinney and the leads of the N3C workstreams for Data Partnerships and Governance, Phenotype and Data Acquisition, and Data Ingestion and Harmonization, and the Palantir platform team (in-kind support only) to implement N3C policies and practices for data governance, transfer, ingestion, storage, and use. He will be a stakeholder collaborator to the "Core Team" of developers. Dr. Williams will also promote the contribution of high-resolution ICU data from other sites to N3C using the mappings from the ETL and disseminate resources that educate N3C users how signal analytic tools can be productively applied to these data. | 202113139349303681 |
| 2020 | Ibm Yorktown Heights, NY | A | $35,924 | paid | Advancing benchmarking and data sharing through crowd-sourced data competitions in cancer research and management. | 202113139349303681 |
Match tier: A Reported EIN · B Exact name and place · C Strong name match · D Probable name match · U Unresolved. Tiers C and D are inferred, not reported; see how matching works.
Recipient matching for this dataset version has not yet completed its independent precision check. Tier A rows carry the EIN the filer reported; tiers B–D are the matcher's inference and should be read as leads until the check is published on the methodology page.
Derived from IRS Form 990 e-file XML. Dataset version 2026.09.0, built 2026-09-03. All years for this funder.
The same organization elsewhere in the program: exempt status and filing health · federal awards · grant guidance · open opportunities.
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