Integration of Ranked Lists via Cross Entropy Monte Carlo with Applications to mRNA and microRNA Studies
暂无分享,去创建一个
[1] T. Barrette,et al. Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. , 2002, Cancer research.
[2] Nicola J. Rinaldi,et al. Computational discovery of gene modules and regulatory networks , 2003, Nature Biotechnology.
[3] T. Barrette,et al. α-Methylacyl-CoA Racemase: Expression Levels of this Novel Cancer Biomarker Depend on Tumor Differentiation , 2002 .
[4] M. Bittner,et al. Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. , 2001, Cancer research.
[5] C. Burge,et al. Prediction of Mammalian MicroRNA Targets , 2003, Cell.
[6] L. Margolin,et al. On the Convergence of the Cross-Entropy Method , 2005, Ann. Oper. Res..
[7] R. Stoughton,et al. Genetics of gene expression surveyed in maize, mouse and man , 2003, Nature.
[8] J. Castle,et al. An integrative genomics approach to infer causal associations between gene expression and disease , 2005, Nature Genetics.
[9] S. Sealfon,et al. Accuracy and calibration of commercial oligonucleotide and custom cDNA microarrays. , 2002, Nucleic acids research.
[10] E. Lander,et al. Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.
[11] Eytan Ruppin,et al. Meta-analysis of gene expression data: a predictor-based approach , 2007, Bioinform..
[12] Ronald Fagin,et al. Comparing top k lists , 2003, SODA '03.
[13] Leroy Hood,et al. A molecular correlate to the Gleason grading system for prostate adenocarcinoma. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[14] Ming Tan,et al. Genome-Wide Tagging SNPs with Entropy-Based Monte Carlo Method , 2006, J. Comput. Biol..
[15] P. Brown,et al. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[16] Anton J. Enright,et al. Human MicroRNA Targets , 2004, PLoS biology.
[17] Jun S. Liu,et al. An algorithm for finding protein–DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments , 2002, Nature Biotechnology.
[18] Reuven Y. Rubinstein,et al. Optimization of computer simulation models with rare events , 1997 .
[19] C. Molony,et al. Genetic analysis of genome-wide variation in human gene expression , 2004, Nature.
[20] S. Dhanasekaran,et al. Delineation of prognostic biomarkers in prostate cancer , 2001, Nature.
[21] Daniel Q. Naiman,et al. Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data , 2005, Bioinform..
[22] Ning Sun,et al. Bayesian error analysis model for reconstructing transcriptional regulatory networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[23] L. McIntyre,et al. Combining mapping and arraying: An approach to candidate gene identification , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[24] Dirk P. Kroese,et al. The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning , 2004 .
[25] Moni Naor,et al. Rank aggregation methods for the Web , 2001, WWW '01.
[26] Giovanni Parmigiani,et al. A Cross-Study Comparison of Gene Expression Studies for the Molecular Classification of Lung Cancer , 2004, Clinical Cancer Research.
[27] J. Welsh,et al. Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. , 2001, Cancer research.
[28] Sangsoo Kim,et al. Combining multiple microarray studies and modeling interstudy variation , 2003, ISMB.
[29] S. Falcon,et al. Combining Results of Microarray Experiments: A Rank Aggregation Approach , 2006, Statistical applications in genetics and molecular biology.
[30] A. Hatzigeorgiou,et al. A combined computational-experimental approach predicts human microRNA targets. , 2004, Genes & development.
[31] Geoffrey J. McLachlan,et al. Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.
[32] O. Klezovitch,et al. Hepsin promotes prostate cancer progression and metastasis. , 2004, Cancer cell.
[33] Anton J. Enright,et al. MicroRNA targets in Drosophila , 2003, Genome Biology.
[34] K. Gunsalus,et al. Combinatorial microRNA target predictions , 2005, Nature Genetics.
[35] Kevin R. Coombes,et al. Differences in gene expression between B-cell chronic lymphocytic leukemia and normal B cells: a meta-analysis of three microarray studies , 2004, Bioinform..
[36] Vasyl Pihur,et al. Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach , 2007, Bioinform..
[37] J. Nelson,et al. Increased fatty acid synthase as a therapeutic target in androgen‐independent prostate cancer progression , 2001, The Prostate.
[38] Ruth Etzioni,et al. Combining Results of Microarray Experiments: A Rank Aggregation Approach , 2006 .
[39] C. Burge,et al. Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets , 2005, Cell.