computational biology

At 33, Dal Co had already founded her own microbial ecology lab at the University of Lausanne in Switzerland.

Despite increasingly strict journal policies requiring the release of computational code files along with research papers, many scientists remain reluctant to share—underscoring the need for better solutions.

With ever-advancing genetic technologies, researchers continue to document the genetic code of the human species.

Thirty years out from the start of the Human Genome Project, researchers have finally finished sequencing the full 3 billion bases of a person’s genetic code. But even a complete reference genome has its shortcomings.

Armed with improved imaging techniques and supercomputers, researchers are generating detailed three-dimensional images of cellular structures that anyone can explore.

More than 3,000 researchers have signed on to a petition that expresses concern over the agency’s 2021 application for the funding program, which emphasizes three areas of computational science and might further disadvantage underrepresented groups.

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.