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Items: 19

1.

Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers.

Zhu B, Song N, Shen R, Arora A, Machiela MJ, Song L, Landi MT, Ghosh D, Chatterjee N, Baladandayuthapani V, Zhao H.

Sci Rep. 2017 Dec 5;7(1):16954. doi: 10.1038/s41598-017-17031-8.

2.

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.

Attallah O, Karthikesalingam A, Holt PJE, Thompson MM, Sayers R, Bown MJ, Choke EC, Ma X.

BMC Med Inform Decis Mak. 2017 Aug 3;17(1):115. doi: 10.1186/s12911-017-0508-3.

3.

More Is Better: Recent Progress in Multi-Omics Data Integration Methods.

Huang S, Chaudhary K, Garmire LX.

Front Genet. 2017 Jun 16;8:84. doi: 10.3389/fgene.2017.00084. eCollection 2017. Review.

4.

Machine learning and systems genomics approaches for multi-omics data.

Lin E, Lane HY.

Biomark Res. 2017 Jan 20;5:2. doi: 10.1186/s40364-017-0082-y. eCollection 2017. Review.

5.

Cancer Progression Prediction Using Gene Interaction Regularized Elastic Net.

Lin Zhang, Hui Liu, Yufei Huang, Xuesong Wang, Yidong Chen, Jia Meng.

IEEE/ACM Trans Comput Biol Bioinform. 2017 Jan-Feb;14(1):145-154. doi: 10.1109/TCBB.2015.2511758. Epub 2015 Dec 23.

6.
7.

Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer.

Pineda S, Real FX, Kogevinas M, Carrato A, Chanock SJ, Malats N, Van Steen K.

PLoS Genet. 2015 Dec 8;11(12):e1005689. doi: 10.1371/journal.pgen.1005689. eCollection 2015 Dec.

8.

Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.

Jeong HH, Leem S, Wee K, Sohn KA.

J Ovarian Res. 2015 Jul 3;8:42. doi: 10.1186/s13048-015-0171-1.

9.

Molecular portraits: the evolution of the concept of transcriptome-based cancer signatures.

Modelska A, Quattrone A, Re A.

Brief Bioinform. 2015 Nov;16(6):1000-7. doi: 10.1093/bib/bbv013. Epub 2015 Mar 31. Review.

10.

Identification of two poorly prognosed ovarian carcinoma subtypes associated with CHEK2 germ-line mutation and non-CHEK2 somatic mutation gene signatures.

Ow GS, Ivshina AV, Fuentes G, Kuznetsov VA.

Cell Cycle. 2014;13(14):2262-80. doi: 10.4161/cc.29271. Epub 2014 May 30.

11.

Empirical chemosensitivity testing in a spheroid model of ovarian cancer using a microfluidics-based multiplex platform.

Das T, Meunier L, Barbe L, Provencher D, Guenat O, Gervais T, Mes-Masson AM.

Biomicrofluidics. 2013 Jan 10;7(1):11805. doi: 10.1063/1.4774309. eCollection 2013.

12.

Identification of ovarian cancer associated genes using an integrated approach in a Boolean framework.

Kumar G, Breen EJ, Ranganathan S.

BMC Syst Biol. 2013 Feb 6;7:12. doi: 10.1186/1752-0509-7-12.

13.

Ovarian cancer : making its own rules-again.

Kohn EC, Hurteau J.

Cancer. 2013 Feb 1;119(3):474-6. doi: 10.1002/cncr.27833. Epub 2012 Dec 11. Review. No abstract available.

14.

A network module-based method for identifying cancer prognostic signatures.

Wu G, Stein L.

Genome Biol. 2012 Dec 10;13(12):R112. doi: 10.1186/gb-2012-13-12-r112.

15.

High quality genomic copy number data from archival formalin-fixed paraffin-embedded leiomyosarcoma: optimisation of universal linkage system labelling.

Salawu A, Ul-Hassan A, Hammond D, Fernando M, Reed M, Sisley K.

PLoS One. 2012;7(11):e50415. doi: 10.1371/journal.pone.0050415. Epub 2012 Nov 29.

16.

Breakthroughs in genomics data integration for predicting clinical outcome.

Lussier YA, Li H.

J Biomed Inform. 2012 Dec;45(6):1199-201. doi: 10.1016/j.jbi.2012.10.003. Epub 2012 Oct 29. No abstract available.

17.

Differing clinical impact of BRCA1 and BRCA2 mutations in serous ovarian cancer.

Liu G, Yang D, Sun Y, Shmulevich I, Xue F, Sood AK, Zhang W.

Pharmacogenomics. 2012 Oct;13(13):1523-35. doi: 10.2217/pgs.12.137. Review.

18.

Identifying multi-layer gene regulatory modules from multi-dimensional genomic data.

Li W, Zhang S, Liu CC, Zhou XJ.

Bioinformatics. 2012 Oct 1;28(19):2458-66. Epub 2012 Aug 3.

19.

Integrated analyses of microRNAs demonstrate their widespread influence on gene expression in high-grade serous ovarian carcinoma.

Creighton CJ, Hernandez-Herrera A, Jacobsen A, Levine DA, Mankoo P, Schultz N, Du Y, Zhang Y, Larsson E, Sheridan R, Xiao W, Spellman PT, Getz G, Wheeler DA, Perou CM, Gibbs RA, Sander C, Hayes DN, Gunaratne PH; Cancer Genome Atlas Research Network.

PLoS One. 2012;7(3):e34546. doi: 10.1371/journal.pone.0034546. Epub 2012 Mar 29.

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