Machine learning identification of < i > Pseudomonas aeruginosa < /i > strains from colony image data

by Jennifer B. Rattray, Ryan J. Lowhorn, Ryan Walden, Pedro M árquez-Zacarías, Evgeniya Molotkova, Gabriel Perron, Claudia Solis-Lemus, Daniel Pimentel Alarcon, Sam P. Brown When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinicalPseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep le...
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research