Machine learning platforms can be fed data to “teach” them to learn patterns and predict outcomes, and they are becoming more important all the time in biomedical research. But what are they? How do they work? And can we peer into the black box of their inner functions to make them more powerful for prediction and discovery?
I wrote about two papers around the new year that presented new computational research tools using convolutional neural networks, or CNNs. These CNNs were trained to recognize useful patterns in reference data sets and validated in real-world applications. While both were applied to cancer, the data inputs (cancer cell imagery and RNA-sequencing data) were vastly different. And, in a way, they represent a kind of shot across the bow. They have made it very obvious that machine learning (ML) is already important in my workplace, JAX, and it is sure to become more so in the months and years ahead. Read more