Researchers characterize large mitochondrial deletions to understand their implications in neurological disorders.

An expert panel will present how synthetic biology accelerates the development of diagnostics, therapeutics, and the medicinal pipeline.

Decoding Smell: Demystifying Human Disease and Behavior

The University of Pittsburgh scientist, who was studying SARS-CoV-2, was shot to death in his home.

Folding@Home, currently focused on deciphering the workings of SARS-CoV-2, is the first project to have exascale-level computational muscle.

The Johns Hopkins University professor was a co-developer of the Galaxy platform, an open-source bioinformatics tool used in labs around the world.

Federal agencies, academic institutions, and industrial partners are joining forces to combat COVID-19 using artificial intelligence.

Researchers’ simulations suggest that small population sizes and inbreeding made Neanderthal populations vulnerable to chance fluctuations in population size.

Neural networks generate abstract images designed to activate particular cells, lending insight into their function.

Machine learning can analyze photographs of cancer, tumor pathology slides, and genomes. Now, scientists are poised to integrate that information into cancer uber-models.

The Carnegie Mellon computational biologist thinks machine learning algorithms can direct high-throughput experiments to solve the field’s unanswered questions.

A brief history of AI, machine learning, artificial neural networks, and deep learning.

Researchers are developing computational approaches to extract information from multiple sets of single-cell sequencing data.

Neuromorphic technology is fueling fast, large-scale simulations, supporting researchers’ endeavors to build models of the human brain.

A deep learning program can identify cells with higher metastatic potential based on the way they look and move.

Researchers have created a 3D-printed artificial neural network that uses light photons to rapidly process information.

Computational biologists are optimistic that the purchase of the world’s largest hub for open-source computer code will not affect the way they use GitHub for science.

The use of underperforming computational tools is a major offender in science’s reproducibility crisis—and there’s growing momentum to avoid it.

Nineteen researchers have stepped down after the journal decided not to retract a paper that they say plagiarized the work of a Johns Hopkins biomedical scientist.