The Bioinformatics Core Facility can participate in your projects via  research-oriented collaborations. We apply state-of-the-art computational approaches and develop novel methods to accelerate biomedical discoveries. Our expertise lies in molecular evolution, machine learning, and big data analytics.

ASU Bioinformatics Core offers project-based collaborations funded through either joint research grants or fee-for-services. The facility scientists currently participate in multiple research collaborations. We welcome new collaborations and can work with you to apply for joint funding. The facility provides collaborative support for large scale multidisciplinary research projects in close coordination with other ASU core facilities including genomics, proteomics, microarray, imaging, and microbiome facilities. Our domain expertise includes:

Translational bioinformatics to assist precision medicine: Apply state-of-the-art bioinformatics tools and design novel methods to translate “Omics” data into discoveries to improve patient care. For example, discover clinical and molecular markers for early diagnosis or treatment optimization; identify potential drug targets for rational drug design; discover immunosignatures to monitor disease progression; and etc.

Machine learning in biomedical studies: Develop advanced machine-learning algorithms for biomedical research. For example, sparse-learning methods to identify clinical and molecular markers associated with a phenotype; deep-learning methods to model complicated biological data; structure-guided learning to incorporate relationships among biomarkers, and etc.

Evolutionary informatics in cancers and complex diseases: Apply theories and methods in phylogenetics and population genetics to study disease etiology. For example, infer subclonal evolution in cancers; investigate genetic patterns of ethnic disparity to address major health concerns for under-represented populations; and etc.

Functional assessment of genetic variations: Computational prediction of functional impact of genetic variations, including protein-coding variants, non-coding variants, and structural variants. We are especially interested in discovery of novel regulatory elements and functional assessment of noncoding variants.