The University of Texas MD Anderson Cancer Center
Department of Bioinformatics and Computational Biology
- Bioinformatics and computational biology
- Machine learning
- Statistical signal processing
My research aims to provide computational methods to explore high-dimensional data, and derive hypotheses and solutions that may lead to discovery of new biology insights. My current interests lie in the area of bioinformatics and computational biology, with a focus on machine learning, statistical signal processing and visualization. A few ongoing projects are:
- Understanding cellular heterogeneity from multi-dimensional single-cell data generated by cytometric technologies, such as flow cytometry, mass cytometry, etc.
- Discovering biological progression underlying a collection of unordered samples, with applications in disease progression and drug response.
- Integrative analysis of data generated by multiple platforms, such as gene expression, methylation, copy number variation, etc.
- Modeling biological systems with differential equations.
Qiu P (2012) Inferring phenotypic properties from single-cell characteristics, PLoS ONE, In Press.
Qiu P, Simonds EF, Bendall SC, Gibbs KD, Bruggner RV, Linderman MD, Sachs K, Nolan GP, Plevritis SK (2011) Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE. Nature Biotechnology. 29(10):886-891.
Qiu P, Gentles AJ, Plevritis SK (2011) Discovering Biological Progression Underlying Microarray Samples",PLoS Computational Biology. 7(4):e1001123.
Qiu P, Plevritis SK (2009) Simultaneous Class Discovery and Classification of Microarray Data Using Spectral Analysis, J Computational Biology. 16(7):935-944.
Office: MDA FCT 4.5002 (Unit 1410)
CV: Click Here to Download
Title: Assistant Professor
Ph.D. - University of Maryland, College Park - 2007