Interpreting personal transcriptomes: Personalized mechanism-scale profiling of RNA-seq data

Pacific Symposium on Biocomputing

Volumn , Issue , 2013, Pages 159-170

  Perez Rathke, Alan ^a   Haiquan, L ^a   Lussier, Yves A ^a  

^a UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSIS; DISEASES; MICROARRAYS; STATISTICAL TESTS;

BIO-MARKER DISCOVERY; BIOLOGICAL MECHANISMS; COMPARATIVE PERFORMANCE; FISHER EXACT TEST; GASTRIC CANCERS; MICROARRAY EXPRESSIONS; PERSONALIZED MEDICINES; STATE OF THE ART;

RNA;

RNA; TRANSCRIPTOME;

BIOLOGY; CLUSTER ANALYSIS; DNA MICROARRAY; GENETICS; HUMAN; NUCLEIC ACID DATABASE; PERSONALIZED MEDICINE; RECEIVER OPERATING CHARACTERISTIC; SEQUENCE ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; STOMACH TUMOR; ARTICLE; STATISTICS;

CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; DATABASES, NUCLEIC ACID; HUMANS; INDIVIDUALIZED MEDICINE; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; RNA, NEOPLASM; ROC CURVE; SEQUENCE ANALYSIS, RNA; STOMACH NEOPLASMS; TRANSCRIPTOME;

EID: 84891436600     PISSN: 23356928     EISSN: 23356936     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (28)

1. Ogata, H., et al, KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research, 1999. 27(1): p. 29-34.
2. Massagué, J., Sorting Out Breast-Cancer Gene Signatures. New England Journal of Medicine, 2007. 356(3): p. 294-297.
3. Chen, J., et al, Protein interaction network underpins concordant prognosis among heterogeneous breast cancer signatures. Journal of Biomedical Informatics, 2010. 43(3): p. 385-396.
4. Chen, J.L., et al, Protein-network modeling of prostate cancer gene signatures reveals essential pathways in disease recurrence. Journal of the American Medical Informatics Association, 2011. 18(4): p. 392-402.
5. Kulasingam, V., M.P. Pavlou, and E.P. Diamandis, Integrating high-throughput technologies in the quest for effective biomarkers for ovarian cancer. Nat Rev Cancer, 2010. 10(5): p. 371-378.
6. Knauer, M., et al, The predictive value of the 70-gene signature for adjuvant chemotherapy in early breast cancer. Breast Cancer Research and Treatment, 2010. 120(3): p. 655-661.
7. Paik, S., et al, A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer. New England Journal of Medicine, 2004. 351(27): p. 2817-2826.
8. Guo, Z., et al, Towards precise classification of cancers based on robust gene functional expression profiles. BMC Bioinformatics, 2005. 6(1): p. 58.
9. Abraham, G., et al, Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context. BMC Bioinformatics, 2010. 11(1): p. 277.
10. Bild, A.H., et al, Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature, 2006. 439(7074): p. 353-357.
11. Chen, X. and L. Wang, Integrating Biological Knowledge with Gene Expression Profiles for Survival Prediction of Cancer. Journal of Computational Biology, 2009. 16(2): p. 265-278.
12. Lee, E., et al, Inferring Pathway Activity toward Precise Disease Classification. PLoS Comput Biol, 2008. 4(11): p. e1000217.
13. Su, J., B.-J. Yoon, and E.R. Dougherty, Accurate and Reliable Cancer Classification Based on Probabilistic Inference of Pathway Activity. PLoS ONE, 2009. 4(12): p. e8161.
14. Yang, X., et al, Single Sample Expression-Anchored Mechanisms Predict Survival in Head and Neck Cancer. PLoS Comput Biol, 2012. 8(1): p. e1002350.
15. Yang, X., et al. Towards Mechanism Classifiers: Expression-anchored Gene Ontology Signature Predicts Clinical Outcome in Lung Adenocarcinoma Patients. in AMIA 2012 Annual Symposium. 2012. Chicago.
16. Lee, Y., et al, Network Modeling Identifies Molecular Functions Targeted by miR-204 to Suppress Head and Neck Tumor Metastasis. PLoS Comput Biol, 2010. 6(4): p. e1000730.
17. Goh, W.W.B., et al, Proteomics Signature Profiling (PSP): A Novel Contextualization Approach for Cancer Proteomics. Journal of Proteome Research, 2012. 11(3): p. 1571-1581.
18. Edgar, R., M. Domrachev, and A.E. Lash, Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research, 2002. 30(1): p. 207-210.
19. Kim, Y.H., et al, AMPKα Modulation in Cancer Progression: Multilayer Integrative Analysis of the Whole Transcriptome in Asian Gastric Cancer. Cancer Research, 2012. 72(10): p. 2512-2521.
20. Mortazavi, A., et al, Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Meth, 2008. 5(7): p. 621-628.
21. Cho, J.Y., et al, Gene Expression Signature-Based Prognostic Risk Score in Gastric Cancer. Clinical Cancer Research, 2011. 17(7): p. 1850-1857.
22. Bolstad, B.M., et al, A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 2003. 19(2): p. 185-193.
23. Marc Carlson, S.F., Herve Pages and Nianhua Li KEGG.db: A set of annotation maps for KEGG.
24. Lottaz, C., et al, OrderedList - a bioconductor package for detecting similarity in ordered gene lists. Bioinformatics, 2006. 22(18): p. 2315-2316.
25. YANG, X., et al, SIMILARITIES OF ORDERED GENE LISTS. Journal of Bioinformatics and Computational Biology, 2006. 04(03): p. 693-708.
26. Subramanian, A., et al, Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America, 2005. 102(43): p. 15545-15550.
27. Tusher, V.G., R. Tibshirani, and G. Chu, Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences, 2001. 98(9): p. 5116-5121.
28. Ward, J.H., Jr., Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 1963. 58(301): p. 236-244.