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AI learns how to recognize and classify different dog breeds from images. A new machine learning method from CZ Biohub now makes it possible to classify and compare different human proteins from fluorescence microscopy images. Credit: CZ Biohub Humans are good at looking at pictures and finding patterns or making comparisons. Take a look at a collection of dog photos, for example, and you can sort them by color, ear size, face shape, and so on. But can you compare them quantitatively? And perhaps more interestingly, can machines extract meaningful information from images that humans cannot? Now Stanford University’s Chan Zuckerberg Biohub team of scientists have developed a machine learning method to quantitatively analyze and compare images—in this case microscopic images of proteins—without any prior knowledge. As reported in Natural Method , their algorithm, dubbed “cytoself”, provides rich and detailed informat...

Biology is rich in patterns. You’ll find them everywhere – from the number of petals (which generally correspond to the number in the Fibonacci sequence), to the number of vertebrae in mammals (giraffes, humans, and quokkas all have seven neck vertebrae). In fact, many viruses follow patterns and have symmetry in their shells. Mathematics is essentially the science of patterns. Patterns can be subtle. So without using math to formally explain and understand it, we can skip it entirely. For a long time, biological research has largely progressed without the sophisticated mathematical modeling that is now at the core of physics, engineering, and climate. But this is changing. Mathematical biology is a growing field that promises to revolutionize microbiology, biotechnology, evolutionary biology and healthcare. With mathematics, scientific breakthroughs that previously required years of trial and error (and tons of trash) can be achieved in a very short time. Here are some of ...