Georgetown University Medical Center

Bioinformatics

Section: Bioinformatics

 

Microarray Group

Typical microarray data are high-dimensional, involve substantial amount of noise, and have small sample size. Expert advice is required to carefully preprocess the raw data and to analyze the expression levels. It is important not only to utilize data analysis tools tailored to the experimental goals, but also to understand the basis for the tools and their limitations. Often, advanced data analysis tools and expertise are needed to effectively extract useful information from microarray data and to enhance data interpretation. The Microarray Group is a team of biostatisticians, bioinformaticians, and computer scientists engaged in developing appropriate experimental design, analysis methods, and data management and sharing systems. The team meets bi-weekly to discuss recent statistical methods and bioinformatics tools for microarray data preprocessing, identification of differentially expressed genes, cluster analysis, and pathway analysis.

Liu Lab

Our research is to advance state of the art in the area of biomedical knowledge management, with an emphasis on literature mining, and in particular natural language processing (NLP) and biomedical ontology development. The lab's daily activities consist of learning, investigation, design, implementation, testing, and evaluation of efficient algorithms and techniques to solve challenging problems in support of a variety of applications including:

  • Advance NLP techniques in biomedicine
  • Ontology-based database integration
  • Microarray data analysis

Areas:
Advance NLP techniques in biomedicine - With the use of computers in storing the explosive amount of biomedical information, natural language processing (NLP) approaches have been explored to make the task of managing information recorded in free text more feasible. We are interested in using corpus-based unsupervised machine learning and online resources to build NLP applications in biomedicine.

Ontology-based database integration - Currently, there are hundreds of biomedical databases. Ontology-based methodologies for database integration promote precise communication between scientists, enable information retrieval across multiple resources, and extend the power of computational approaches to perform data exploration, inference and mining. We promote the use of data mining and text mining techniques for ontology development and relationship establishment.

Microarray data analysis - DNA microarray technology has provided an opportunity to simultaneously monitor the expression levels of a large number of genes in response to intentional experiment perturbations such as gene disruptions and drug treatments. We conduct research on upper-stream analysis including probe set redefinition, probe-level analysis, and down-streaming analysis using Gene Ontology.


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