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Advancing ALS Discovery through Artificial Intelligence and Natural Language Processing

ALS Finding A Cure (ASLFAC)

ALS Finding a Cure® is pleased to announce a Request for Proposals (RFP) to support innovative research projects leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) to advance ALS discovery. This funding opportunity seeks to catalyze transformative research that integrates AI-driven methodologies to improve diagnosis, biomarker identification, disease progression modeling, and therapeutic target discovery in ALS.

Background:

ALS is a devastating neurodegenerative disease with no cure and limited therapeutic options. AI and NLP offer the potential to revolutionize ALS research by enabling the extraction of meaningful insights from complex datasets, including electronic health records (EHRs), published medical literature, clinical trial data, omics datasets, and real-world patient-reported data. Through this RFP, we aim to support high-impact projects that will enhance our understanding of ALS and accelerate novel therapeutic strategies.

Examples of large data sources include but are not limited to:
Answer ALS, ALS Compute, UK Bio Bank. ProACT, NeuroBank, Project MINE

Scope of the RFP:

We invite applications proposing AI and/or NLP-based approaches to address critical challenges in ALS research. Areas of interest include, but are not limited to:

Drug Discovery and Repurposing: Machine learning-based identification of potential ALS drug targets or repurposed therapies using AI-driven drug screening techniques.
Biomarker Discovery and Validation: Integration of AI/NLP with multi-omics, imaging, and patient-reported outcomes to identify and validate ALS biomarkers.
AI-Driven Early Diagnosis: Use of AI and NLP for automated extraction of early clinical features from EHRs, speech and language analysis, or fluid biomarker or imaging data to improve early detection of ALS.
Pathomechanistic Knowledge Graphs: Integration of NLP tools with the published medical literature for discovery of previously underappreciated mechanistic targets.
Disease Progression Modeling: AI-based analysis of longitudinal clinical and /or biomarker data to enhance prediction models for disease progression and stratification of patients.
Optimizing Clinical Trials: AI-powered patient stratification, automated NLP-based extraction of clinical endpoints from records, virtual comparison groups and trial recruitment optimization.

Data Subtypes: Use of AI technology to separate ALS from Controls and to find ALS subtypes in the Answer ALS data set by combining clinical, biomarker, genetic and motor neuron omics data.

Funding and Award Information:

Grant Duration: Up to 2 years
Funding Amount: Up to $400,000 USD total (inclusive of both direct and indirect costs)
Number of Awards: 3 awarded teams
Indirect Costs: Maximum of 15%

Application Timeline:

Initial Letter of Interest (LOI) Submission Deadline: Friday May 23, 2025
Notification of LOI Submission: July 2025
Full Proposals Due: Wednesday August 8, 2025

Project Start Date: Wednesday October 1, 2025

Review Criteria:

Proposals will be evaluated based on:

Scientific Merit and Innovation: Potential of AI/NLP methodologies to transform ALS research.
Feasibility and Approach: Strength of study design, methodology, and data integration strategy.
Investigative Team: Expertise and collaborative potential of the research team.
Translational Potential: Likelihood of findings contributing to improved ALS diagnostics, treatments, or patient outcomes.

AI Based Application Success Predictor

1. Fit with ALSFAC’s Strategic Priorities 🎯

Your proposal should align closely with ALSFAC’s current Request for Proposals (RFPs), such as:

AI/NLP-driven projects targeting drug discovery, early diagnosis, biomarker identification, or disease modeling

Early-diagnostics innovation, employing digital, imaging, electrophysiologic, or molecular strategies

2. Translational & High-Impact Science

Seek transformative impact: e.g., accelerating diagnosis, improving patient stratification, or uncovering therapeutic targets.

Use relevant ALS data platforms (e.g., Answer ALS, UK Biobank, ProACT) to support mechanistic and biomarker insights 

3. Strong, Multidisciplinary Team

ALSFAC favors collaborations that pair disease experts with computational/AI specialists, and/or clinicians, biostatisticians, and patient advocates 

Prior successful grants (e.g., Arrest ALS, Answer ALS) show robust multi-institutional and multidisciplinary leadership 

4. Feasibility & Milestones-Focused Plan

Include detailed project timelines and defined milestones tied to funding phases (e.g., LOI → full proposal).

Demonstrate access to data and infrastructure required for execution 

5. Clear Budgeting & Cost Compliance

Budget up to US $400 K over 2 years (AI/NLP RFP) or $500 K over 3 years (Early Diagnostics), including ≤15% indirect costs

Ensure itemization aligns with ALSFAC guidelines and is supported by justification in full proposal

6. Two-Stage, Peer-Review Selection

Stage 1 (LOI): Focus on clarity, innovation, team strength—supported by strong Letter of Interest 

Stage 2 (Full Proposal): Includes detailed study plan, budget, CVs, data-access confirmation, and human-readable abstract for lay audiences

7. Global Eligibility & Collaboration Support

Open worldwide—academic, clinical, and industry groups are all eligible; partnerships across sectors are encouraged 

✅ Success Roadmap

Predictor	Action Plan
Strategic fit	Map proposal to current RFP objectives (AI/NLP or diagnostics)
High-impact science	Emphasize clinical relevance, biomarkers, or drug targets
Multidisciplinary team	Partner disease experts with AI, clinical, and statistical collaborators
Feasibility & milestones	Provide clear plans and confirm access to data infrastructure
Budget compliance	Adhere to funding caps and justify all costs, including indirects
Two-phase readiness	Draft strong LOI (≤2 pages), prepare full proposal content for next stage
Global & sector inclusion	Involve international and translational collaborators

🔧 Pro Tips for ASLFAC Applications:

Submit a concise, compelling LOI (max 2 pages): highlight innovation, team strengths, and expected impact.

Showcase your access to key data platforms like Answer ALS or EHR datasets.

Partner early with AI/NLP experts—interdisciplinary teams are critical.

Plan detailed milestones with measurable deliverables and associated costs.

Use clear language in abstract and lay summary — proposals may be shared publicly.

Follow stage deadlines precisely—LOI due May 23, 2025; full proposals typically due in August 

Specialties

Artificial Intelligence

Eligibility Requirements

Open to researchers at academic, medical, and research institutions worldwide.
Multidisciplinary and collaborative proposals integrating AI/NLP expertise with ALS clinical or biological research are encouraged.
Prior AI/NLP experience is not required, but partnerships with computational experts are recommended.

Pharmaceutical & Clinical Collaboration: Synergistic collaboration between industry and institutions with data-driven models encouraged

Sponsor Details

Sponsor Institute/Organizations: ALS Finding A Cure (ASLFAC)

Sponsor Type: Corporate/Non-Profit

Address: 1014 Gateway Boulevard, Suite 105 Boynton Beach, FL 33426

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