Using systems biology, investigators put cancer risk SNPs in a network context, identifying immune, tumor-related, and other genes influenced by the variants in multiple tissue types.
The company uses systems biology approaches and AI methods to model and analyze causal pathways in diseases including neurological cancers and Alzheimers.
A large genome-wide association meta-analysis and follow-up analyses revealed eight risk loci involving SNPs previously associated with body traits, metabolism, and psychiatric risk.
Researchers brought together blood transcriptomic, metabolomic, proteomic, and immune cell profiles for dozens of West African and Australasian infants.
Using omics and other data, researchers performed network analyses that pointed to interactions between human herpesviruses and other Alzheimer's disease risk factors.
Through a genome-wide association study, meta-analysis, and systems analysis, researchers saw enhanced common variant distribution in mutation-intolerant genes.
Using pathway and network analyses, researchers identified overlapping genes and biological processes behind smoking and schizophrenia, including new candidate genes.
Participants tried to classify samples based on the epigenomic impact of cigarette smoke cessation and aerosol from modified- or reduced-risk products.
The agency has committed $11 million for the creation of a network of research centers that will use systems biology approaches to build predictive models for infectious diseases.
