Research Interests

• Data summarization: Usually the sizes of biological databases are huge. This reduces the usability of the information hidden in such data. I would like to explore efficient and meaningful summarization methods.
• Integration: Different types of biological data reveal different information about life. (e.g. sequences, motifs, structures, etc.) I would like to develop new methods that combines these data to better understand how different organisms work.
• Indexing: For many problems, exhaustive searching of bioinformatics data is unfeasible due to the size of the data. A number of index structures have been proposed to avoid exhaustive searches. However, these index structures are either too large or inaccurate. I would like to find novel indexing methods, that are small, efficient, dynamic, and extendible to different types of data.
• Computational methods for pharmacogenomics: In the near future, we will be able to obtain the complete genetic code for individuals. This will enable development of personalized medicine. Understanding and managing data at this scale requires efficient data management, statistical analysis, and learning methods.
• Graph model for bioinformatics: Most of the major bioinformatics problems can be reduced to well known graph theory problems. I would like to use this to find a more common data model for different types of bioinformatics data. This will make it easier to share information across heterogeneous data.
• Inferring function from raw data: The functional properties of an important fraction of the existing proteins are unknown. There is a need for computational methods that helps to discover these properties from the raw data.

Funding

Students

• Xuehui Li
• Jun Liu (Supervisor: Dr. Ranka)
• Padmavati Sridhar
• Jayendra Venkateswaran
• Xu Zhang

Conferences