Broad Institute Autoimmune Disease

Crunch Foundation, The Eric and Wendy Schmidt Center, and The Klarman Cell Observatory invite you to join the Autoimmune Disease ML Challenge to design algorithms to help millions of people.

Challenge Overview

Introduction

Autoimmune diseases arise when the immune system mistakenly targets healthy cells. Affecting 50M people in the U.S., with rising global cases, Inflammatory Bowel Disease (IBD) is one of the most prevalent forms. IBD occurs when the barrier between our gut and the microbes living there breaks down, leading to the activation of the immune system and persistent inflammation. This cycle of flares and remission increases the risk of colorectal cancer (up to two-fold). Although modern treatments have improved survival, IBD remains challenging to diagnose and treat due to its complex pathogenic pathways and multifactorial nature.

Pathologists rely on gut tissue images to diagnose and treat IBD, guiding decisions on the most suitable drug treatments and predicting cancer risk. These tissue images, combined with recent advances in genomics, offer a valuable dataset for machine learning models to revolutionize IBD diagnosis and treatment.

Read the full competition specifications here.

This challenge is meant for everyone! We have created a three-lecture crash course that provides background on the biology, technology, and data in the three crunches. You do not need a background in biology or medicine to participate.

Find the lecture crash course here.

Phases

The challenge is broken down into three Crunches, ordered by increasing complexity.

Crunch 1 – Oct 28 to Feb 9 – Predict gene expression in spatial transcriptomics data from matched pathology images

Crunchers will build a model to predict the expression of 460 genes in held-out patches of colon tissue using H&E pathology images and Xenium spatial transcriptomics training data. Hematoxylin and Eosin (H&E) images provide insight into cell organization, while Xenium data add information on gene expression and cellular pathways of disease.

Crunch 2 – Nov 18 to Mar 21 – Predicting Unseen Genes

In this phase, participants will predict the expression of all protein-coding genes, including those that were not measured in the spatial training data, using single-cell RNA-seq data as support. This Crunch focuses on leveraging cell transcriptional profiles to enhance the predictive model’s ability to infer the expression of unknown genes in spatial contexts.

Crunch 3 – Dec 9 to Apr 30 (submission deadline) / May 15 (peer review deadline) – Identifying Gene Markers for Pre-cancerous Regions

Participants will rank genes by their ability to distinguish between dysplasia (pre-cancerous) regions and noncancerous tissue in IBD patients, increasing our ability to detect cancer early. The final gene panel will be chosen based on participant performance in Crunch 2 and on peer review of participants' methods taking place after the submission deadline. The gene panel will be experimentally validated in a new colon tissue with dysplasia, and all participants' ranked gene lists will be scored.

The best-performing models will be experimentally tested to validate their ability to predict cancer risk, which could lead to early detection and improved treatment options. The best models will be publish in an official publication from Broad Institute.

Participant Output Requirements

For each Crunch, participants must submit predictions in CSV format. Each submission must adhere to the provided log1p-normalization standards.

Outputs will be evaluated using:

• Mean Squared Error (Crunch 1). To avoid overfitting Crunch will score all submitted models on a private dataset during two Checkpoints.

• Spearman’s Correlation (Crunch 2)

• Accuracy and Diversity Metrics (Crunch 3)

Evaluation Criteria

Performance will be evaluated through:

• Accuracy in gene expression prediction (Crunch 1 & 2)

• Gene panel design for distinguishing between noncancerous and dysplasia regions (Crunch 3)

• Diversity of selected gene programs in Crunch 3, with extra emphasis on identifying unique biological pathways

• Peer review of methods to select dysplasia gene panel in Crunch 3

Prizes

Crunch 1

Winner’s rank	Prize value (in USD)
1st Place	3,500
2nd Place	2,500
3rd Place	1,750
4th Place	900
5th Place	750
6th Place	700
7th Place	600
8th Place	500
9th Place	450
10th Place	350
Total	12,000

Crunch 2

Winner’s rank	Prize value (in USD)
1st Place	3,500
2nd Place	2,500
3rd Place	1,750
4th Place	900
5th Place	750
6th Place	700
7th Place	600
8th Place	500
9th Place	450
10th Place	350
Total	12,000

Crunch 3

Winner’s rank	Prize value (in USD)
1st Place	7,000
2nd Place	6,500
3rd Place	5,000
4th Place	3,000
5th Place	1,000
6th Place	900
7th Place	800
8th Place	700
9th Place	600
10th Place	500
Total	26,000

External Resources

Crunchers are encouraged to use publicly available external resources, including gene expression datasets and pre-trained models, as long as they are properly credited.

A list of potential resources and references is provided in the full challenge specifications.

Foundry Institute offers a computing environment with $10 USD equivalent to around 10h of GPU time.

Find ML Foundry documentation here.