Novel structural co-expression analysis linking the NPM1-associated ribosomal biogenesis network to chronic myelogenous leukemia
暂无分享,去创建一个
Xihong Lin | Fengfeng Wang | Xihong Lin | W. Cho | Fengfeng Wang | P. Siu | Nancy B Y Tsui | William CS Cho | Lawrence WC Chan | Godwin Yung | Thomas Lui | Ya Ming Chiu | Nancy BY Tsui | SP Yip | Parco M. Siu | SC Cesar Wong | Benjamin YM Yung | N. Tsui | B. Yung | Godwin Yung | Y. Chiu | S. Yip | L. Chan | Thomas Lui | S. Wong
[1] A. Bishayee,et al. Resveratrol-mediated chemoprevention of diethylnitrosamine-initiated hepatocarcinogenesis: inhibition of cell proliferation and induction of apoptosis. , 2009, Chemico-biological interactions.
[2] R. Fisher. On the Interpretation of χ2 from Contingency Tables, and the Calculation of P , 2010 .
[3] D. Gutmann,et al. TSC1 sets the rate of ribosome export and protein synthesis through nucleophosmin translation. , 2007, Cancer research.
[4] Jun Dong,et al. Geometric Interpretation of Gene Coexpression Network Analysis , 2008, PLoS Comput. Biol..
[5] Lynne M Connelly,et al. Fisher's Exact Test. , 2016, Medsurg nursing : official journal of the Academy of Medical-Surgical Nurses.
[6] Lincoln Stein,et al. Reactome: a database of reactions, pathways and biological processes , 2010, Nucleic Acids Res..
[7] M. Gerstein,et al. Genomic analysis of gene expression relationships in transcriptional regulatory networks. , 2003, Trends in genetics : TIG.
[8] R. Fisher. On the Interpretation of χ2 from Contingency Tables, and the Calculation of P , 2018, Journal of the Royal Statistical Society Series A (Statistics in Society).
[9] Pablo Tamayo,et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[10] M. Ashburner,et al. Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.
[11] E. S. Pearson. Biometrika tables for statisticians , 1967 .
[12] Beau Dabbs,et al. Summary and discussion of : “ Controlling the False Discovery Rate : A Practical and Powerful Approach to Multiple Testing , 2014 .
[13] B. Williams,et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq , 2008, Nature Methods.
[14] Y. Yen,et al. Mechanistic Control of Carcinoembryonic Antigen-related Cell Adhesion Molecule-1 (CEACAM1) Splice Isoforms by the Heterogeneous Nuclear Ribonuclear Proteins hnRNP L, hnRNP A1, and hnRNP M* , 2011, The Journal of Biological Chemistry.
[15] Christina Kendziorski,et al. Statistical methods for gene set co-expression analysis , 2009, Bioinform..
[16] B. Druker. Overcoming resistance to imatinib by combining targeted agents. , 2003, Molecular cancer therapeutics.
[17] Antonio Reverter,et al. A Differential Wiring Analysis of Expression Data Correctly Identifies the Gene Containing the Causal Mutation , 2009, PLoS Comput. Biol..
[18] Benno Schwikowski,et al. Discovering regulatory and signalling circuits in molecular interaction networks , 2002, ISMB.
[19] Hiromitsu Araki,et al. GeneSetDB: A comprehensive meta-database, statistical and visualisation framework for gene set analysis , 2012, FEBS open bio.
[20] H. Hotelling. New Light on the Correlation Coefficient and its Transforms , 1953 .
[21] John D. Storey,et al. Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[22] P. Pandolfi,et al. Nucleophosmin Serves as a Rate-Limiting Nuclear Export Chaperone for the Mammalian Ribosome , 2008, Molecular and Cellular Biology.
[23] K. Lange,et al. Genetics of early-onset obsessive–compulsive disorder , 2010, European Child & Adolescent Psychiatry.
[24] Y. Baran,et al. Apoptotic effects of resveratrol, a grape polyphenol, on imatinib-sensitive and resistant K562 chronic myeloid leukemia cells. , 2012, Anticancer research.
[25] Magdalena Szumilas. Explaining odds ratios. , 2010, Journal of the Canadian Academy of Child and Adolescent Psychiatry = Journal de l'Academie canadienne de psychiatrie de l'enfant et de l'adolescent.
[26] Dan A. Biddle,et al. Using Lancaster's mid-P correction to the Fisher's exact test for adverse impact analyses. , 2011, The Journal of applied psychology.
[27] Sameer Al-harthi,et al. Modulation of doxorubicin cytotoxicity by resveratrol in a human breast cancer cell line , 2012, Cancer Cell International.
[28] Marcio Luis Acencio,et al. The generation and utilization of a cancer-oriented representation of the human transcriptome by using expressed sequence tags , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[29] Mathieu Blanchette,et al. PReMod: a database of genome-wide mammalian cis-regulatory module predictions , 2006, Nucleic Acids Res..
[30] Antonio Marco,et al. Relationship between gene co-expression and sharing of transcription factor binding sites in Drosophila melanogaster , 2009, Bioinform..
[31] B. Brors,et al. Molecular signature of CD34+ hematopoietic stem and progenitor cells of patients with CML in chronic phase , 2007, Leukemia.
[32] Daniela Cilloni,et al. Molecular Pathways: BCR-ABL , 2011, Clinical Cancer Research.
[33] John D. Storey. A direct approach to false discovery rates , 2002 .
[34] Y. Liu,et al. B23 acts as a nucleolar stress sensor and promotes cell survival through its dynamic interaction with hnRNPU and hnRNPA1 , 2010, Oncogene.
[35] Pier Paolo Pandolfi,et al. Nucleophosmin and cancer , 2006, Nature Reviews Cancer.
[36] Dong Xu,et al. Pathway Correlation Profile of Gene-Gene Co-Expression for Identifying Pathway Perturbation , 2012, PloS one.
[37] Matej Oresic,et al. Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process , 2007, Bioinform..
[38] S. Horvath,et al. Weighted gene coexpression network analysis strategies applied to mouse weight , 2007, Mammalian Genome.
[39] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[40] Homin K. Lee,et al. Coexpression analysis of human genes across many microarray data sets. , 2004, Genome research.
[41] Martin Vingron,et al. Variance stabilization applied to microarray data calibration and to the quantification of differential expression , 2002, ISMB.