Volume 4 Issue 2, February 2024

We highlight the challenges and opportunities in organic redox flow battery research, underscoring the need for collaborative research efforts. The synergy between computation and experimentation holds the potential to expedite progress in this field and can have far-reaching impacts beyond energy storage applications.

One of the greatest limitations of deep neural networks is the difficulty of interpreting what they learn from the data. Deep distilling addresses this problem by extracting human-comprehensible and executable code from observations.

A method for correcting errors in the spatial-genetic reconstruction of DNA microscopy is proposed, leading to more accurate results and potential to resolve new biology.

As computation is increasingly integrated into drug research and development, this Perspective analyzes company business models, funding and deals to provide unique insights into risks and opportunities in this quickly maturing industry, which aims to expedite the creation of life-saving therapeutics.

kmindex is a tool able to index thousands of environmental metagenomes and perform sequence searches in a fraction of a second, thus enabling real-time queries on complex genomic datasets.

Automated algorithm discovery has been difficult for artificial intelligence given the immense search space of possible functions. Here explainable neural networks are used to discover algorithms that outperform those designed by humans.

DNA microscopy reconstructs the spatial organization of a sample from a neighborhood graph. In this work, MinIPath efficiently corrects errors from these graphs that distort the reconstruction, both in simulated and experimental data.

The authors introduce two cellular barcoding tools: CellBarcode, for extracting and filtering diverse DNA barcodes from bulk and single-cell sequencing data; and CellBarcodeSim, for simulating barcoding experiments, thus enabling the investigation of the impact of biological and technical factors on barcode detection.