Building Biology with Machine Learning GEN Genetic Engineering & Biotechnology News - Biotech from Bench to Business GEN

The tech world has embraced Machine Learning (ML) for its powerful intuitive capabilities--to increase click-through rates on ads, sell more books, and help you keep in touch with mom. Despite being increasingly common as a classification tool in applications ranging from transcriptomics, metabolomics, and neuronal synaptic activities, ML is still almost absent in the area of bioengineering. Why is that and what can we do to increase ML use in bioengineering? Machine Learning algorithms that date back half a century are now commonly used for pattern-based analysis, including Decision Trees, Nearest Neighbors, Neural Nets, and more recently with significant success Deep Learning--a version of Neural Net with more layers and more nodes--received significant attention when it won against the best human in the ancient Chinese game of Go. Deep Learning has been enabled by access to new powerful computational hardware, in particular the graphical processing units (GPUs) originally developed for the gaming industry. These gaming GPUs allow for massively parallel computations, which is perfect for ML applications.