Powerful machine-learning technique uncovers unknown features of pathogen

PHILADELPHIA — A powerful new machine-learning technique can be applied to large datasets in the biological sciences to uncover previously unknown features of organisms and their genes, according to a team led by researchers from the Perelman School of Medicine at the University of Pennsylvania. For example, the technique learned the characteristic gene-expression patterns that appear when a pathogenic bacterium is exposed to low-oxygen conditions and robustly identified changes that occur in response to antibiotics. The technique employs a recently developed algorithm called a “denoising autoencoder,” which learns to identify recurrent features or patterns in large datasets without being told what specific features to look for. In 2012, for instance, when Google-sponsored researchers applied a similar method to randomly selected YouTube images, their system successfully learned to recognize major recurrent…


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