When the levee breaks: a practical guide to sketching algorithms for processing the flood of genomic data | Genome Biology
Considerable advances in genomics over the past decade have resulted in vast amounts of data being generated and deposited in global archives. The growth of these archives exceeds our ability to process their content, leading to significant analysis bottlenecks. Sketching algorithms produce small, approximate summaries of data and have shown great utility in tackling this flood of genomic data, while using minimal compute resources. This article reviews the current state of the field, focusing on how the algorithms work and how genomicists can utilize them effectively.
Superior skin cancer classification by the combination of human and artificial intelligence | European Journal of Cancer
This article describes the first experiment on combining human and artificial intelligence for the classification of images suspicious of skin cancer. The combination achieved a superior accuracy of 82.95% (compared to 81.59%/42.94% achieved by artificial/human intelligence alone)
Unsupervised data to content transformation with histogram-matching cycle-consistent generative adversarial networks | Nature Machine Intelligence
The segmentation of images is a common task in a broad range of research fields. To tackle increasingly complex images, artificial intelligence-based approaches have emerged to overcome the shortcomings of traditional feature detection methods. Inspired by the ability of cycle-consistent generative adversarial networks to perform style transfer, researchers outline a method whereby a computer-generated set of images is used to segment the true images. They benchmark their unsupervised approach against a state-of-the-art supervised cell-counting network on the VGG Cells dataset and show that it is not only competitive but also able to precisely locate individual cells.