Single-cell RNA-seq AI analysis has become the default way to make sense of the millions of expression measurements a single experiment can now generate. Turning raw sequencing counts into ...

A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...

On Wednesday, Jelani Nelson, a professor of theoretical computer science and chair of UC Berkeley's electrical engineering and computer science division, announced he was taking a leave of absence to ...

Abstract: Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data ...

MIT researchers found that different algorithms can all be grouped into a ‘periodic table’ of AI. The idea for the table was an accident that emerged from identifying similarities between two ...

Abstract: Time series analysis is a critical task across various scientific and industrial domains, enabling the extraction of valuable insights from temporal data. High dimensionality of time series ...

ABSTRACT: This review focuses on the recent advancements in neuroimaging enabled by deep learning techniques, specifically highlighting their applications in brain disorder detection and diagnosis.

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...

Thyroid cancer incidences endure to increase even though a large number of inspection tools have been developed recently. Since there is no standard and certain procedure to follow for the thyroid ...