Computational biologists apply theoretical principles and develop analysis and inference methods to decipher and understand biological systems. They are experts in quantitative approaches, data science and algorithms, while also having in-depth knowledge of complex biological processes. Particularly in cancer biology, there is a growing need for computational biology leaders with expertise and understanding in both quantitative and biological sciences. 
  
The purpose of the Damon Runyon Quantitative Biology Fellowship is to identify computational scientists who innovatively combine quantitative analyses and modeling to solve biological questions. To achieve this goal the award program will support computational scientists who are deeply connected to biology labs so that they can bring their computational skills to bear on important biological questions, particularly in cancer. Additionally, the proposed mentored research training must reflect the individual's potential to develop into a productive, independent cancer research scientist. 
  
This award program encourages scientists, trained in fields such as mathematics, computer science, physics, engineering, and/or a related field, to pursue research careers in computational biology under the joint mentorship of leaders in both computational science (“dry lab”) and cancer biology (“wet lab”). By investing in this area, Damon Runyon will bring additional attention to the importance of these specially trained scientists for making meaningful progress in cancer biology. The Foundation encourages all theoretical and experimental research relevant to the study of cancer and the search for cancer causes, mechanisms, therapies and prevention including molecular approaches to cancer prevention for inherited cancers.

Selection and Review

The Quantitative Biology Fellowship Award Committee, comprised of leaders in the field, will review applications and select awardees for approval by the Damon Runyon Cancer Research Foundation’s Board of Directors.

Applications will be evaluated by the following criteria:

• The quality of the applicant, their commitment to pursuing a research career as a computational biologist, and the potential to excel in cancer research.
• The capacity of the mentorship team ( two required) to provide a robust training experience that will accelerate the development of the applicant’s scientific skills and prepare them to independently conduct high quality, innovative cancer-related research. Preference will be shown toward mentorship labs that are in close physical proximity to the applicant. The goal is to encourage frequent in person meetings and training in each lab. The labs may be located at the same or distinct institutions.
• Each Mentor, one in a “dry lab” and one in a “wet lab” setting, must have generated a proven track record in the successful training of other scientists.
• The qualifications, experience, and productivity of both the applicant and the Mentors.
• The quality of the research proposal (importance of the problem, originality of approach, appropriateness of techniques and clarity of presentation).
• Quality of the level of computational innovation introduced to solve the proposed problem. While building your own tools for analysis is not required, preference will be shown toward projects that create new or introduce novelty to existing technology.

At the beginning of each review session, the following statement is read aloud: 

As an organization, the Damon Runyon Cancer Research Foundation has an uncompromising commitment to excellence and values diversity, equity, and inclusion in all of its Award Programs. The Foundation seeks to promote gender equality and increase diversity, in all of its forms, throughout our programs. Studies have demonstrated that often subtle and unconscious biases can dramatically affect outcomes in review processes and that by simply acknowledging that unconscious biases exist, we can combat their potential negative impact. To that end, please be aware of your potential unconscious biases when reviewing, scoring and discussing candidates and applications throughout the review process. 

AI Based Application Success Predictor

1. ⭐⭐⭐⭐⭐ Exceptional scientific innovation

This is the strongest predictor across the Damon Runyon portfolio.

The Rachleff Innovation Award explicitly states that incremental research will not be funded and seeks exceptionally creative ideas with potential for significant impact on cancer prevention, diagnosis, treatment or fundamental understanding.

A strong proposal should introduce something genuinely different:

• New biological mechanism
• New therapeutic concept
• New diagnostic approach
• New technology
• New disease model
• New computational strategy
• New conceptual framework

The key question is:

What is fundamentally different about this research compared with what the field is already doing?

2. ⭐⭐⭐⭐⭐ Potential for major impact on cancer

Innovation must translate into significance.

Damon Runyon evaluates whether proposed research could significantly advance:

• Cancer prevention
• Cancer diagnosis
• Cancer treatment
• Basic understanding of cancer biology. 

A competitive proposal should therefore clearly describe the potential consequence of success.

Weak:

"We will identify another pathway involved in tumor growth."

Stronger:

"We will determine whether an unexplored dependency creates a therapeutic vulnerability that could overcome resistance to current treatment."

The reviewer should understand what could change if the project succeeds.

3. ⭐⭐⭐⭐⭐ Bold, high-risk/high-reward research

Damon Runyon deliberately funds research that may be too unconventional or insufficiently mature for traditional funding.

The Rachleff Innovation Award is explicitly designed for high-risk/high-reward projects where the investigator has an innovative idea but lacks enough preliminary data to obtain conventional funding.

This means:

High scientific risk + enormous potential payoff

can be a strength.

But:

High risk + no credible scientific rationale

is not.

The proposal needs to show why the risk is justified and what experiments can test the central idea.

4. ⭐⭐⭐⭐⭐ Exceptional applicant potential

Damon Runyon is fundamentally an investigator-development funder.

Its eight award programs are designed around identifying and supporting promising young cancer researchers.

The selection committees are composed of internationally recognized scientists who review applications and recommend awardees to the Foundation's Board.

Reviewers therefore consider whether the applicant has the potential to become a future scientific leader, not simply whether the proposed experiment is technically competent.

Important evidence includes:

• Strong publications.
• Original scientific contributions.
• Intellectual independence.
• Clear research trajectory.
• Technical expertise.
• Evidence of unusual scientific creativity.
• Commitment to cancer research.

5. ⭐⭐⭐⭐⭐ Strong fit between applicant, idea and career stage

Damon Runyon has deliberately differentiated award mechanisms.

For example:

The Foundation's current program portfolio reflects this career-stage structure.

Therefore:

Excellent science + incorrect career-stage mechanism = poor strategic fit.

6. ⭐⭐⭐⭐⭐ Strong connection between the investigator's track record and proposed breakthrough

The applicant should make a convincing intellectual progression:

Past research → new observation/insight → proposed breakthrough

For the Innovation Award, the Foundation evaluates the applicant's capacity to conduct bold, exceptionally creative research in addition to evaluating the research itself.

The best proposals make reviewers think:

"This particular investigator is unusually well positioned to discover whether this idea works."

7. ⭐⭐⭐⭐☆ Scientific rigor supporting an ambitious hypothesis

High-risk research does not mean weak methodology.

A competitive proposal should establish:

• Strong scientific rationale.
• Testable hypothesis.
• Appropriate models.
• Appropriate controls.
• Quantitative endpoints.
• Alternative approaches.
• Appropriate statistical methods.
• A logical experimental sequence.

For the Clinical Investigator Award, the Foundation specifically evaluates innovation, creativity, quality and originality of the research proposal along with the importance of the research.

8. ⭐⭐⭐⭐☆ Translational or clinical significance where appropriate

This predictor is particularly important for the Clinical Investigator Award and translational projects.

The Clinical Investigator Award is intended for young physician-scientists conducting disease-oriented research aimed at improving cancer medicine. Selection includes:

• Commitment to clinical/translational/cancer-prevention research.
• Ability to apply laboratory advances to clinical problems.
• Importance of the proposed research to cancer/cancer prevention. 

Strong translational projects should establish:

Mechanism → biomarker/target → intervention → clinical relevance

Not every Damon Runyon project must immediately reach patients, however; the Fellowship and Innovation programs also support fundamental cancer biology.

9. ⭐⭐⭐⭐☆ Independence and future scientific leadership

Damon Runyon wants its awards to launch long-term scientific careers.

The Foundation describes its awards as investments in the next generation of cancer researchers, with the objective of enabling scientists to pursue bold research where conventional mechanisms may not provide adequate support.

For early-career applicants, a strong proposal should demonstrate:

• Increasing scientific independence.
• A distinct research niche.
• Ability to lead a research program.
• Appropriate mentorship.
• Long-term cancer research commitment.
• Potential to attract future external funding.

10. ⭐⭐⭐⭐☆ Need for Damon Runyon funding

This is unusually explicit for the Rachleff Innovation Award.

The Innovation Award evaluates the applicant's lack of resources to pursue the proposed research, and the award is designed for projects that lack sufficient preliminary data for traditional funding.

The proposal should therefore make clear:

Why is this project difficult to pursue through conventional funding right now?

A compelling answer might be:

• Concept is unconventional.
• Preliminary data are insufficient for NIH-style funding.
• Project requires an exploratory proof-of-concept phase.
• Conventional funding mechanisms are too conservative for the proposed risk.