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      Gene and pathway identification with L p penalized Bayesian logistic regression

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          Abstract

          Background

          Identifying genes and pathways associated with diseases such as cancer has been a subject of considerable research in recent years in the area of bioinformatics and computational biology. It has been demonstrated that the magnitude of differential expression does not necessarily indicate biological significance. Even a very small change in the expression of particular gene may have dramatic physiological consequences if the protein encoded by this gene plays a catalytic role in a specific cell function. Moreover, highly correlated genes may function together on the same pathway biologically. Finally, in sparse logistic regression with L p ( p < 1) penalty, the degree of the sparsity obtained is determined by the value of the regularization parameter. Usually this parameter must be carefully tuned through cross-validation, which is time consuming.

          Results

          In this paper, we proposed a simple Bayesian approach to integrate the regularization parameter out analytically using a new prior. Therefore, there is no longer a need for parameter selection, as it is eliminated entirely from the model. The proposed algorithm (BLpLog) is typically two or three orders of magnitude faster than the original algorithm and free from bias in performance estimation. We also define a novel similarity measure and develop an integrated algorithm to hunt the regulatory genes with low expression changes but having high correlation with the selected genes. Pathways of those correlated genes were identified with DAVID http://david.abcc.ncifcrf.gov/.

          Conclusion

          Experimental results with gene expression data demonstrate that the proposed methods can be utilized to identify important genes and pathways that are related to cancer and build a parsimonious model for future patient predictions.

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          Most cited references23

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          Sparsity and smoothness via the fused lasso

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            Gene expression profiling predicts clinical outcome of breast cancer.

            Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.
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              Discussion of "Least angle regression" by Efron et al

              (2004)
              Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2008
                3 October 2008
                : 9
                : 412
                Affiliations
                [1 ]Division of Biostatistics, University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA
                [2 ]Department of Medicine and Greenebaum Cancer Center, The University of Maryland School of Medicine, Baltimore, MD 21201, USA
                [3 ]Department of Pathology, The University of Maryland School of Medicine, Balitimore, MD 21201, USA
                Article
                1471-2105-9-412
                10.1186/1471-2105-9-412
                2567351
                18834526
                b7fff2be-d9a2-41d4-aa61-5beb58a7efc6
                Copyright © 2008 Liu et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 April 2008
                : 3 October 2008
                Categories
                Methodology Article

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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