The study of ubiquitous circadian rhythms in human physiology requires regular measurements across time. Repeated sampling of the different internal tissues that house circadian clocks is both practically and ethically infeasible. Here, we present a ...
With the growth of artificial intelligence (AI)-ready datasets such as the National Health and Nutrition Examination Survey (NHANES), new opportunities for data-driven research are being created, but also generating risks of data exploitation by pape...
Multiplexed imaging is a powerful approach in spatial biology, although it is complex, expensive and labor-intensive. Here, we present the IBEX Knowledge-Base, a central resource for reagents, protocols and more, to enhance knowledge sharing, optimiz...
With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. We discuss the challenges and stat...
The rise of antibiotic resistance calls for innovative solutions. The realization that biology can be mined digitally using artificial intelligence has revealed a new paradigm for antibiotic discovery, offering hope in the fight against superbugs.
Transport proteins play a crucial role in cellular metabolism and are central to many aspects of molecular biology and medicine. Determining the function of transport proteins experimentally is challenging, as they become unstable when isolated from ...
Here we propose "molecular connectomics" to link molecular and morphological cell features in three dimensions across scales, using machine learning and artificial intelligence to reveal emergent properties of complex biological systems.
Molecular Biology has long tried to discover mechanisms, considering that unless we understand the principles, we cannot develop applications. Now machine learning and artificial intelligence enable direct leaps to application without understanding t...
Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive model...
Comprehensive analysis of tissue cell type composition using microscopic techniques has primarily been confined to ex vivo approaches. Here, we introduce NuCLear (Nucleus-instructed tissue composition using deep learning), an approach combining in vi...