Redundancy in Benchmarks

Abraham delves into the issue of data redundancy in machine learning benchmarks, highlighting how it can lead to overfitting and teaching to the test rather than true progress. By partnering with experts in various fields, they aim to validate their approach across different proteins and diseases, emphasizing the importance of real-world applicability over theoretical performance.