Diseases with High Funding But Affect Few People

Build a structured disease knowledge graph focused on conditions that receive high research funding and attention despite affecting relatively small populations.

The objective of this bounty is to ingest, normalize, and interlink heterogeneous biomedical, epidemiological, pharmacological, and financial data sources into Geo, creating a high-quality, queryable dataset.

This dataset will capture the imbalance between:

  • Disease prevalence

  • Research attention

  • Funding allocation

  • Drug development activity

  • Regulatory and market incentives

Objective

  • Ingest data from multiple fragmented sources (ICD, OMIM, Orphanet, funding databases, etc.)

  • Normalize inconsistent disease, drug, and funding data

  • Create a standardized ontology layer

  • Interlink diseases with clinical, epidemiological, therapeutic, and financial attributes

  • Enable cross-disease analysis of funding vs population impact

Rationale (Why this bounty exists):

  • Not all diseases receive funding proportional to their burden.

  • Some diseases affecting few people (often rare or orphan diseases) receive significant funding due to:

  • Strong advocacy groups and patient communities

  • Orphan drug incentives that encourage pharmaceutical investment

  • High commercial potential per patient (premium drug pricing)

  • Scientific or genetic research interest

  • Strategic importance in biotechnology and innovation

As a result:

  • Certain rare diseases are highly funded

  • Many common diseases remain underfunded or under-researched

  • However, this information is fragmented and difficult to analyze because:

  • Clinical data and funding data are not linked

  • Epidemiological metrics are not connected to financial data

  • Drug development data is siloed

This bounty aims to unify these layers into a single structured knowledge graph.

Ontology Fields

Text Fields

  • Description

  • Causes

  • Pathophysiology

  • Disease progression

  • Prognosis

Relational Fields

  • Diseases → Diseases

  • Disease categories and subtypes → Disease category

  • Alias → Alternative names

  • Symptoms → Symptoms

  • Body systems → Body systems

  • Severity scales → Severity

  • Complications → Conditions

  • Comorbidities → Conditions

  • Risk factors → Risk factors

  • Trigger factors → Trigger factors

  • Transmission routes → Transmission routes

  • Prevention strategies → Preventive measures

  • Geographical distribution → Countries / Regions

  • Treatment guidelines → Treatment Guidelines

  • Drug roles → Drugs

  • Drug outcomes → Therapeutic effects

  • Side effects caused by drugs → Conditions

  • Therapeutic areas → Therapeutic areas

  • Research attention → Research category

  • Regulatory status → Regulatory category

  • Market incentives → Incentives

  • Funding source → Organizations

  • Related drugs → Drugs

  • Orphan drugs → Drugs

  • Key researchers and contributors → People

  • ICD-10 code → ICD-10

  • ICD-11 code → ICD-11

  • OMIM number → OMIM number

  • Orphanet ID → Orphanet ID

Numeric Fields

  • Prevalence

  • Incidence

  • Mortality rate

  • Total funding (USD)

Boolean Field

  • Is rare disease - Yes/No

Scope

Structure a dataset of 100 to 300 diseases that receive high funding despite affecting few people.

Key Challenges

  • Fragmentation of data across multiple biomedical and financial sources

  • Lack of standardized linking between funding and disease burden

  • Difficulty in aligning epidemiological metrics with research attention

  • Inconsistent naming and identifiers across databases

  • Limited interoperability between clinical, drug, and funding datasets

Expected Outcome

  • A structured, queryable disease knowledge graph that enables:

  • Identification of diseases with high funding but low prevalence

  • Analysis of funding disparities in healthcare

  • Mapping of drug development focus areas

  • Exploration of orphan disease ecosystems

  • Cross-linking of funding, clinical outcomes, and epidemiology

Applications

  • Disease intelligence dashboards

  • Funding allocation analysis tools

  • Rare disease research platforms

  • Drug repurposing and development analysis

  • Healthcare policy and prioritization systems

Why It Matters

  • Global health priorities are not always aligned with real-world disease burden.

  • By structuring and linking this data:

  • Funding inequalities become visible

  • Underserved diseases can be identified

  • Research priorities can be improved

  • Better decisions can be made in healthcare policy and drug development

Difficulty

Hard / Expert bounty

Requirements

  • Strong understanding of:

  • Biomedical ontologies

  • Data normalization and entity resolution

  • Healthcare funding ecosystems

  • Drug development pipelines

  • Graph data modeling

Sources

  • ICD-10 and ICD-11 classification systems

  • OMIM (Online Mendelian Inheritance in Man)

  • Orphanet rare disease database

  • WHO and CDC datasets

  • Global funding databases (NIH and others)

  • Clinical trial registries

  • RxNorm

  • PubMed and scientific literature


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