Six things to keep in mind while reading biology ML papers

The article discusses six key considerations when reading biology machine learning papers. It highlights the challenges of relying on established benchmarks, the importance of critically evaluating baseline comparisons, and the prevalence of curiosity-driven research in the field. The article emphasizes the need to understand the limitations of assays used in research and the potential biases in evaluations. It also touches on the issue of data leakage in pair-based problems like molecule-protein interactions. The authors caution against naively accepting strong results on benchmarks and stress the significance of recognizing implicit limitations in research methodologies. They suggest a more critical approach to assessing the real-world impact of novel approaches in biology ML. The article aims to provide readers with a nuanced perspective on interpreting and evaluating biology machine learning papers, encouraging a thoughtful and informed reading approach.