The Su Lab
Welcome to the home page for the lab of Andrew Su. Our group works in the field of bioinformatics, and is located at the Scripps Research Institute in sunny La Jolla, California. Please browse around the site to get an overview of our research, to meet our team members, and to read our blog.
Our group embraces the data mining challenges that have resulted from high-throughput, multi-dimensional and multi-modal biology. We have many ongoing research projects that span multiple disease areas in collaborations with some of the world’s experts in these areas.
We have a strong interest in in making biomedical knowledge more Findable, Accessible, Interoperable, and Reusable (F.A.I.R.). In support of this goal, we build user friendly tools, resources, and infrastructure to support the bleeding edge of biomedical research.
We harness the collective efforts of the community (both researchers and the general public) towards solving grand challenges in biology. These “community intelligence” initiatives are powerful because they scale with the explosive growth of data generation in science.
The laboratory of Dr. Andrew Su works in a highly interwoven collaboration with the laboratory of Dr. Chunlei Wu. Our research is primarily funded by NIH grants to Andrew, Chunlei and to our many collaborators here at Scripps Research and at outside institutions.
How can you add new data sources using the BioThings SDK to make them more FAIR?
What is the BioThings SDK? How can you use it to quickly create an API and make your data more F.A.I.R.?
Although there has been a proliferation of biological datasets made available in recent years, often this information isn’t machine readable, making it hard for things like Google Dataset Search to find and index them. In this series of blog posts, we’ll outline how...
Who We Are
Andrew is a Professor at the Scripps Research Institute in the Department of Integrative, Structural and Computational Biology. His research focuses on building and applying bioinformatics infrastructure for biomedical discovery.
We are a motley crew joined together by a common belief in open and participatory science. Our backgrounds are diverse and we believe that these different talents and expertise build a whole that is greater than the sum of its parts.
We collaborate with some experts across many different disciplines of research from multiple areas of disease research to machine learning and crowdsourced knowledge management.
Are you interested in doing highly collaborative science with both experimental and computational colleagues? We are always interested in recruiting talented individuals to join our team. If you fit that description, please get in touch!