IBM and the Broad Institute will codevelop algorithms to apply artificial intelligence to predict cardiovascular risk from genomic and clinical data.
The machine-learning-based method identifies relationships between bacterial strains and tracks their movements in less time, using less memory than existing solutions.
The solution uses machine learning to pare down somatic variant lists, simplifying the task of manually reviewing the output of sequence analysis pipelines.
The company has not released data on its method, but describes a circulating tumor DNA approach that has similarities to what other firms are pursuing.
Using genotype and phenotype data from the UK Biobank project, a Stanford investigator uncovered variants linked to low bone mineral density.
Using the method, the researchers predicted the effects of over 140 million mutations in different tissues and identified mutations possibly associated with increased risk of several immune diseases.
The team came up with an algorithm called bloodTyper for antigen-typing based on whole-genome sequences.
MIT and Stanford computer scientists developed a technique for secure, massively scalable genomic analysis that they hope will unleash greater data sharing.
In Genome Biology this week: transcription factor use among brittle stars, single-cell RNA sequencing strategy, and more.
The latest release of Sophia AI merges imaging analysis with genomic medicine in pursuit of better diagnosis and treatment.
The World Health Organization has announced the members of its gene-editing committee, according to NPR.
DARPA is working on developing algorithms that gauge the credibility of research findings, Wired reports.
The American Society of Breast Surgeons recommends all women diagnosed with breast cancer be offered genetic testing, the Washington Post says.
In Science this week: comparison of modern, historical rabbit exomes uncovers parallel evolution after myxoma virus exposure; and more.