12-24-2025, 01:59 PM
I'm a postdoc in molecular biology, and our lab is starting to explore how machine learning in scientific research can help us analyze high-throughput microscopy images to identify subtle cellular phenotypes that are difficult to quantify manually. We have a decent dataset, but none of us are ML experts, and we're overwhelmed by the choice of models, from simpler convolutional neural networks to more complex architectures, and the challenge of curating and labeling our training data effectively. For researchers who have successfully integrated ML into wet-lab science, what was your learning path and which resources or collaborations were most critical for bridging the domain knowledge gap? How did you manage the computational requirements and validate that your model's predictions were biologically meaningful rather than just statistical artifacts of your specific dataset?