Funders › PA › Every Cure Inc › 2024
Grants paid by Every Cure Inc, tax year 2024
EIN 92-0240717 · Philadelphia, PA · Form 990, Schedule I · NTEE H99
In tax year 2024, Every Cure Inc (EIN 92-0240717) reported 5 grants paid totaling $2,181,501. Dataset version 2026.09.0, built 2026-09-03.
Every grant, 2024
| Tax year | Recipient | Match | Amount | Type | Purpose | Source filing |
|---|---|---|---|---|---|---|
| 2024 | University of North Carolina Chapel Hill, NJ | A | $971,372 | paid | UNC WILL CO-LEAD DEVELOPMENT OF THE INTEGRATION LAYER THAT CONNECTS DIVERSE DATASETS INTO A UNIFIED BIOMEDICAL KNOWLEDGE GRAPH. THEY WILL DEFINE ONTOLOGY STANDARDS, CONDUCT MANUAL CURATION, AND LEAD HARMONIZATION ACROSS DRUG, DISEASE, AND MOLECULAR DATA TYPES. THEIR ROLE ENSURES THE ROBOKOP AND MONARCH KNOWLEDGE GRAPHS AND PROPRIETARY DATASETS ARE SYSTEMATICALLY ALIGNED TO EVERY CURE'S COMPUTATIONAL INFRASTRUCTURE. | 202513009349301626 |
| 2024 | The Scripps Research Institute La Jolla, CA | A | $362,247 | paid | SCRIPPS WILL INTEGRATE ITS BIOTHINGS EXPLORER PLATFORM INTO EVERY CURE'S KNOWLEDGE GRAPH, CONTRIBUTING APIS, DATA EVALUATION, AND ONTOLOGY MAPPING EXPERTISE. THEY WILL IDENTIFY PROPRIETARY DATASETS, EVALUATE THEIR UTILITY, AND REFINE INTEGRATION PROCESSES FOR CLINICAL AND PATIENT-LEVEL DATA. THEIR WORK STRENGTHENS INTEROPERABILITY AND ENSURES HIGH-VALUE BIOMEDICAL DATA IS CONNECTED ACROSS THE MATRIX PROJECT. | 202513009349301626 |
| 2024 | Institute for Systems Biology Seattle, WA | A | $309,022 | paid | ISB WILL CURATE, EVALUATE, AND INTEGRATE LARGE BIOMEDICAL DATASETS INTO EVERY CURE'S KNOWLEDGE GRAPH, FOCUSING ON DATA QUALITY AND ONTOLOGY HARMONIZATION. THEY WILL PERFORM MANUAL CURATION, CROSS-VALIDATION, AND DEVELOPMENT OF PROTOCOLS TO ENSURE RELIABLE DRUG, DISEASE, AND MOLECULAR ASSOCIATIONS. THEIR WORK ENSURES THE SPOKE DATABASE AND OTHER KEY SOURCES ARE INCORPORATED INTO A ROBUST COMPUTATIONAL FRAMEWORK FOR DRUG REPURPOSING. | 202513009349301626 |
| 2024 | Penn State University University Park, PA | A | $303,699 | paid | PENN STATE WILL LEAD THE DEVELOPMENT OF ADVANCED COMPUTATIONAL TOOLS, INCLUDING ALGORITHMS TO PREDICT DRUG REPURPOSING OPPORTUNITIES WITHIN THE KNOWLEDGE GRAPH. THEY WILL WORK ON DATA EXTRACTION, HARMONIZATION, AND MACHINE LEARNING MODEL DEVELOPMENT TO RANK CANDIDATE DRUGS. THEIR ROLE PROVIDES THE COMPUTATIONAL "ENGINE" THAT DRIVES PRIORITIZATION OF PROMISING THERAPIES. | 202513009349301626 |
| 2024 | University of Alabama Birmingham Birmingham, AL | A | $235,161 | paid | UAB WILL CONTRIBUTE SPECIALIZED BIOMEDICAL DATA AND EXPERTISE IN INTEGRATING PATIENT-LEVEL, GENOMIC, AND CLINICAL DATASETS INTO THE KNOWLEDGE GRAPH. THEY WILL SUPPORT VALIDATION OF PREDICTED DRUG-DISEASE ASSOCIATIONS THROUGH COMPUTATIONAL AND EXPERIMENTAL METHODS. THEIR WORK ENSURES THE KNOWLEDGE GRAPH IS GROUNDED IN HIGH-QUALITY CLINICAL AND BIOLOGICAL EVIDENCE. | 202513009349301626 |
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.
This is informational only, derived from public data on the dates shown. It is not an eligibility determination, and not legal, tax, or accounting advice. Verify against the official source before relying on it.