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Do you have a passion for computational biology, predictive AI, or genomics and want to make an impact in a rare disease? Do you know that one out of 10 people globally is affected by a rare disease? Hack with us for neurofibromatosis (NF), a rare genetic disorder that affects 1 in 3,000 people globally each year. 

Gather your team and get ready to compete for a variety of cash prizes with a total value of $25,000 in 1 of 4 provided challenges. Use datasets, engage with patients, and receive mentorship from experts in the field. Join us and patients all over the world in the hack to #EndNF.


The data sets are available now at hack4nf.org/data!
Join the slack channel here!

Challenge #1

GENIE-NF tumor identification and classification challenge


Use the provided Genomics Evidence Neoplasia Information Exchange (GENIE) datasets provided to develop a new framework that accurately uses genomic data to classify tumor samples for neurofibromatosis-related tumors. First, identify the neurofibromatosis-related tumors in the dataset - this could be defined as all tumors with mutations in NF-relevant genes, or tumors that are common in patients with NF, or another rational definition. Then, use one or more classification methods to classify the tumor samples into different groups based on genetic features. 


Early access to version 13 of the GENIE dataset for hackathon registrants. You are encouraged to also use other external datasets, but you must include the GENIE dataset in your project. 

Example Solutions:

A classification algorithm that differentiates different types of NF1, NF2, and schwannomatosis-related tumors using clinical sequencing data. A list of the most important features in your algorithm for differentiating tumor types.  

Challenge #2

Devising in silico strategies to prioritize likely pathogenic NF1 germline variants


Given a germline variant dataset from neurofibromatosis type 1, develop a strategy to score the pathogenicity of individual NF1 variants. Your solution may combine features of the variant, predictions and findings from external data sources, AlphaFold protein structure predictions, literature-mining, or other evidence sources to assign a severity score to any NF1 variant. We recommend that participants read this recent preprint focused on how to rigorously test missense prediction algorithms.


We will provide a list of germline NF1 variants from the Leiden Open Variation Database. Any external methods or databases you might want to leverage (e.g. PolyPhen, SIFT, COSMIC, LOVD, etc).

Example Solutions:

Apply your computational algorithm to rank the variants from the provided list from most severe to least severe impact. Please provide a description of your algorithm and a code repository containing all relevant code.

Challenge #3

In silico drug target screening for NF


Use variant, gene expression, or drug screening data from different sources and apply already-published or new methods to predict drug targets that could be helpful in treating manifestations of NF1, NF2, or schwannomatosis.


Publicly accessible drug screen data on tumors, processed variant or gene expression data from different sources (e.g. NF Data Portal, cBioPortal, GENIE, Pediatric Brain Tumor Atlas), any other datasets that you can find. 

Example Solutions:

All teams must produce a ranked list of drugs for NF1-related tumors, NF2-related tumors, and/or schwannomatosis-related tumors. Organizers will aggregate the ranked lists and will publish this list openly (e.g., F1000) for the NF research community to mine.

Wildcard Challenge

Have an idea that uses one of these datasets? Go for it! However, ideas must be pre approved by the organizing committee. Submit your idea by October 21, 2022 to be eligible for a Wild Card project. 


Aug. 28th

Registration Opens!

Oct. 14th

Pre-Kickoff Webinar


Oct. 15th

Hackathon Begins

Oct. 15th


Kickoff Webinar

Oct. 17th


Team Formation Webinar

Guest presentation: OpenCRAVAT

Oct. 21st


Final Submissions Due

Nov. 4th

11:59 PM EST

Nov. 8th

Project Presentations


Nov. 16th

Winners Announced


Up to $25,000 in PRIZES!


1st Place Challenge Winners

One team from each of the 3 Challenge Categories will be selected for 1st Place and awarded $1,500.


Best use of the PMP platform

One team will be awarded for the best use of the PMP platform.


Best Project Page

One team will be awarded for the best use of their project page.


Best Use of Data

One team will be awarded for the best use of data provided for their challenge.

$5k to 


Incubation Prizes 

From $5,000, up to $15,000 total incubation cash prizes are available.

The American Heart Association’s Precision Medicine Platform is a supporter of the Children’s Tumor Foundation’s Neurofibromatosis Hackathon.





  • Publish a project page by November 4, 2022.
  • 3 minute video linked to the project page.
  • Publicly viewable code repository (such as github).
  • If applying for Incubation, a 3 month plan.
  • Provide a detailed overview of the background of your project, Methods, results, future directions, and any relevant links.
  • Provide which data sets you utilized for your project.

Teams competing for the incubation prizes are required to submit (together with their most completed project), a 3-6 months plan (800 words max) that includes the following sections: a brief project summary, impact of their solution, a plan for the development of their project during the incubation period with clear goals, statements for time commitment from team members. The plan has to be added to the project documentation and clearly mentioned in a specific “submission for incubation” section within the project.