Data Science at JAX

Data science at JAX is a transdisciplinary research ecosystem that brings together scientists from the fields of computational biology, systems biology, bioinformatics, and statistics to develop tools and data resources for interpreting large, complex data sets.

Images represent a large proportion of all measurements made in biomedical research, including microscopy slides and 3-D magnetic resonance (MRI) or computed tomography (CT) images. Automated image analysis can interpret image data of high volume and complexity in an efficient and reproducible way. For training opportunities in image analysis, visit Data Science Training & Events.

CKB is a dynamic digital resource for interpreting complex cancer genomic profiles. Visit CKB website.

Machine learning (ML) extracts knowledge from data and focuses on prediction. ML learns from our data how to make decisions for future observations including applications in science, such as personalized cancer treatment, medical diagnoses, and drug discovery. Learn more at Scikit.

The JAX Genetic Diversity Initiative is a strategic effort to increase the awareness and availability of diversity mouse populations and associated tools across the research community. These populations are powering a revolution in model organism research that will enable major advancements in the understanding of complex genetic traits.

The JAX Bioinformatics Training Program aims to build data science talent at JAX by providing training in scientific computing and data analysis.

The Human Phenotype Ontology (HPO) project provides an ontology of medically relevant phenotypes, disease-phenotype annotations, and the algorithms that operate on these.

Data Science Highlights