data mining

Computational scientists develop a system for spotting data overdue for public release, and end up getting hundreds of open-access datasets corrected.

In today’s data-heavy research environment, wet-lab scientists can benefit from new computational skills.

After spending more than a decade developing tools to study patterns in gene sequences, bioinformaticians are now working on programs to analyze epigenomics data.

Nature’s publisher launches a new peer-reviewed, online-only journal that will accept descriptions of data sets.

From research results to electronic health records, biomedical data are becoming increasingly accessible. How can scientists best capitalize on the information deluge?

With scientific budgets eroding, the biomedical community needs to get more return from the data it has already generated.

Computational challenges are tapping the collective wisdom of the scientific community to solve medicine’s biggest problems.

Last week brought scientists one step closer to outlining a plan for the massive government-funded project.  

Despite increasing use of electronic medical records, much patient data remains in text form, requiring text-mining techniques to make full use of patient information.

Computer programs that trawl research papers can reveal important large-scale patterns and facilitate further research, but publishers are wary.

Although fully organized patient-run trials are still few and far between, patients are taking a more active role in clinical research.

Patients are sidestepping clinical research and using themselves as guinea pigs to test new treatments for fatal diseases. Will they hurt themselves, or science?

The largest collection of genetic and medical data in the United States links telomeres and genetic variants to longevity and disease.

Making sense of the data deluge

Giving researchers access to the health records of 52 million people in England could prove invaluable to biomedical scientists.