Dr. Olshen's research interests lie in the fields of statistical genomics and bioinformatics. He is particularly interested in examining DNA copy number data derived from microarrays. A paper on a change-point method for analyzing these data has recently appeared. Further work with DNA copy number includes comparing it to expression data and building prediction rules from it. Another research interest is trying to understand the differences in gene expression data among institutions and over time. A final project is the use of tree-structured vector quantization (TSVQ) to cluster protein data.
Dr. Olshen is also involved in a number of interesting collaborations, many of which include microarray experiments. He is part of program projects in breast cancer and pediatrics. He is searching for lymphoma target genes in collaboration with Dr. Raju Chaganti. He is working with Dr. Jeff Boyd on high-throughput genotyping of breast cancer. Finally, he is deriving predictive peptide patterns in blood serum with Dr. Paul Tempst and others.
Selected Bibliography:
1. Olshen AB, O'Sullivan F. Camouflaged Deconvolution with application to blood curve modeling in FDG PET studies. Journal of the American Statistical Association 1997; 92:1293-1303.
2. Olshen AB, Jain AN. Deriving quantitative conclusions from microaaay expression data. Bioinformatics 2002;18:961-970.
3. Lucito R, Healy J, Alexander J, Reiner A, Espositio D, Chi M, Rodgers L, Brady A, Sebat J, Troge J, West JA, Rostan S, Nguyen KC, Powers S, Ye KQ, Olshen AB, Venkatraman E, Norton L, Wigler M. Representational oligonucleotide microarray analysis: a high resolution method to detect genome copy number variation. Genome Research 2003;13:2291-2305.
4. Olshen AB, Venkatraman ES, Lucito R, Wigler M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 2004; 4:557-572.
5. Olshen AB, Cosman PC, Rodrigo AG, Bickel PJ, Olshen RA. Vector quantization of amino acids: Analysis of the HIV V3 loop region. Journal of Statistical Planning and Inference 2005; 130:277-298.
