Achievements

Here are listed the data science challenges we participated in as a team.

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Autoimmune Disease Machine Learning Challenge

by Eric and Wendy Schmidt Center at the Broad Institute, USA
Part 1: Oktober 2024 – February 2025

Part 2: November 2024 – March 2025

Part 3: December 2024 – April 2025

Part 1: 🏆 2th Place / 75 submissions / 7800 participants – 2400$ Prize

Part 2: 🏆 6th Place / 11 submissions / 8200 participants – 700$ Prize

Part 3: 🏆 still ongoing

Learn more about the challenge

Overview
Autoimmune diseases occur when the immune system mistakenly attacks healthy cells, disrupting the body’s natural defense mechanisms. These diseases affect 50 million people in the U.S., with cases rising globally. One of the most prevalent autoimmune diseases is inflammatory bowel disease (IBD). To better understand and treat IBD, researchers sequence the RNA (transcriptome) of individual affected cells. However, this process is highly complex and expensive, making a predictive model for transcriptomics a valuable tool in advancing research. This challenge consisted of three independent sub-challenges, each addressing a distinct aspect of this broader research problem:

• The task of Part 1 of this challenge was to create a model that can deliver this expensive and hard to obtain information from readily available pathology images.
• The task of Part 2 was to predict all genes of a cell from a (predicted) subpart.
• The task of Part 3 was to rank all genes based on their ability to distinguish between normal cells and ones showing dysplasia.

Approach
For task 1, we used the ResNet50 model implemented in PyTorch to directly
map from the input images to the 460 genes (end-to-end deep learning approach).

For task 2, we took our trained ResNet50 model and its predictions, which served as the input to a standard neural network, which mapped from the 460 predicted genes to all genes of the cell.

For task 3, we submitted many different approaches. Notable examples include (i) determining the AUC value for all features, and sorting them according to that, and (ii) predicting the cells with dysplasia with the help of a neural network model, for which we extracted feature importances with the Integrated Gradients method from the Captum library. Integrated Gradients is a feature importance algorithm for neural networks.