Lei Xu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Lei Xu has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Aik Choon Tan
Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
2 shared publications - Donald Geman
Department of Applied Mathematics and Statistics, Johns Hopkins University
2 shared publications - Raimond L. Winslow
Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States of America
2 shared publications Daniel Q. Naiman
The Whitaker Biomedical Engineering Institute, The Johns Hopkins University Baltimore MD 21218, USA
1 shared publicationDonald Geman
The Whitaker Biomedical Engineering Institute, The Johns Hopkins University Baltimore MD 21218, USA
1 shared publicationRaimond L. Winslow
The Whitaker Biomedical Engineering Institute, The Johns Hopkins University Baltimore MD 21218, USA
1 shared publication
Latest Publications
- Open Access
- 27 February 2008
- Published by Springer Nature in BMC Bioinformatics
- Vol. 9 (1) , 125
Merging microarray data from separate breast cancer studies provides a robust prognostic test
There is an urgent need for new prognostic markers of breast cancer metastases to ensure that newly diagnosed patients receive appropriate therapy. Recent studies have demonstrated the potential value of gene expression signatures in assessing the risk of developing distant metastases. However, due to the small sample sizes of individual studies, the overlap among signatures is almost zero and their predictive power is often limited. Integrating microarray data from multiple studies in order to increase sample size is therefore a promising approach to the development of more robust prognostic tests.
- 27 February 2008
- Published by Springer Nature in BMC Bioinformatics
- Vol. 9 (1) , 125
Open AccessMerging microarray data from separate breast cancer studies provides a robust prognostic test
There is an urgent need for new prognostic markers of breast cancer metastases to ensure that newly diagnosed patients receive appropriate therapy. Recent studies have demonstrated the potential value of gene expression signatures in assessing the risk of developing distant metastases. However, due to the small sample sizes of individual studies, the overlap among signatures is almost zero and their predictive power is often limited. Integrating microarray data from multiple studies in order to increase sample size is therefore a promising approach to the development of more robust prognostic tests.
- Open Access
- 30 July 2007
- Published by Springer Nature in BMC Bioinformatics
- Vol. 8 (1) , 275
Large-scale integration of cancer microarray data identifies a robust common cancer signature
There is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis. DNA microarrays provide a means for measuring gene expression signatures which can then be used as components of genomic-based diagnostic tests to determine the presence of cancer.
- 30 July 2007
- Published by Springer Nature in BMC Bioinformatics
- Vol. 8 (1) , 275
Open AccessLarge-scale integration of cancer microarray data identifies a robust common cancer signature
There is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis. DNA microarrays provide a means for measuring gene expression signatures which can then be used as components of genomic-based diagnostic tests to determine the presence of cancer.
- Open Access
- 30 August 2005
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 21 (20) , 3905-3911
Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data
Motivation: DNA microarray data analysis has been used previously to identify marker genes which discriminate cancer from normal samples. However, due to the limited sample size of each study, there are few common markers among different studies of the same cancer. With the rapid accumulation of microarray data, it is of great interest to integrate inter-study microarray data to increase sample size, which could lead to the discovery of more reliable markers. Results: We present a novel, simple method of integrating different microarray datasets to identify marker genes and apply the method to prostate cancer datasets. In this study, by applying a new statistical method, referred to as the top-scoring pair (TSP) classifier, we have identified a pair of robust marker genes (HPN and STAT6) by integrating microarray datasets from three different prostate cancer studies. Cross-platform validation shows that the TSP classifier built from the marker gene pair, which simply compares relative expression values, achieves high accuracy, sensitivity and specificity on independent datasets generated using various array platforms. Our findings suggest a new model for the discovery of marker genes from accumulated microarray data and demonstrate how the great wealth of microarray data can be exploited to increase the power of statistical analysis. Contact:leixu@jhu.edu
- 30 August 2005
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 21 (20) , 3905-3911
Open AccessRobust prostate cancer marker genes emerge from direct integration of inter-study microarray data
Motivation: DNA microarray data analysis has been used previously to identify marker genes which discriminate cancer from normal samples. However, due to the limited sample size of each study, there are few common markers among different studies of the same cancer. With the rapid accumulation of microarray data, it is of great interest to integrate inter-study microarray data to increase sample size, which could lead to the discovery of more reliable markers. Results: We present a novel, simple method of integrating different microarray datasets to identify marker genes and apply the method to prostate cancer datasets. In this study, by applying a new statistical method, referred to as the top-scoring pair (TSP) classifier, we have identified a pair of robust marker genes (HPN and STAT6) by integrating microarray datasets from three different prostate cancer studies. Cross-platform validation shows that the TSP classifier built from the marker gene pair, which simply compares relative expression values, achieves high accuracy, sensitivity and specificity on independent datasets generated using various array platforms. Our findings suggest a new model for the discovery of marker genes from accumulated microarray data and demonstrate how the great wealth of microarray data can be exploited to increase the power of statistical analysis. Contact:leixu@jhu.edu