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Items: 1 to 20 of 167

1.

Using knowledge-driven genomic interactions for multi-omics data analysis: metadimensional models for predicting clinical outcomes in ovarian carcinoma.

Kim D, Li R, Lucas A, Verma SS, Dudek SM, Ritchie MD.

J Am Med Inform Assoc. 2017 May 1;24(3):577-587. doi: 10.1093/jamia/ocw165.

2.

Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.

Kim D, Joung JG, Sohn KA, Shin H, Park YR, Ritchie MD, Kim JH.

J Am Med Inform Assoc. 2015 Jan;22(1):109-20. doi: 10.1136/amiajnl-2013-002481. Epub 2014 Jul 7.

3.

Incorporating inter-relationships between different levels of genomic data into cancer clinical outcome prediction.

Kim D, Shin H, Sohn KA, Verma A, Ritchie MD, Kim JH.

Methods. 2014 Jun 1;67(3):344-53. doi: 10.1016/j.ymeth.2014.02.003. Epub 2014 Feb 18.

4.

Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.

Kim D, Li R, Dudek SM, Ritchie MD.

J Biomed Inform. 2015 Aug;56:220-8. doi: 10.1016/j.jbi.2015.05.019. Epub 2015 Jun 3.

5.

Knowledge-driven genomic interactions: an application in ovarian cancer.

Kim D, Li R, Dudek SM, Frase AT, Pendergrass SA, Ritchie MD.

BioData Min. 2014 Sep 9;7:20. doi: 10.1186/1756-0381-7-20. eCollection 2014.

6.

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.

7.

A multivariate approach to the integration of multi-omics datasets.

Meng C, Kuster B, Culhane AC, Gholami AM.

BMC Bioinformatics. 2014 May 29;15:162. doi: 10.1186/1471-2105-15-162.

8.

Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles.

Mankoo PK, Shen R, Schultz N, Levine DA, Sander C.

PLoS One. 2011;6(11):e24709. doi: 10.1371/journal.pone.0024709. Epub 2011 Nov 3.

9.

Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.

Zhang W, Ota T, Shridhar V, Chien J, Wu B, Kuang R.

PLoS Comput Biol. 2013;9(3):e1002975. doi: 10.1371/journal.pcbi.1002975. Epub 2013 Mar 21.

10.

Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach.

Zhang X, Li Y, Akinyemiju T, Ojesina AI, Buckhaults P, Liu N, Xu B, Yi N.

Genetics. 2017 Jan;205(1):89-100. doi: 10.1534/genetics.116.189191. Epub 2016 Nov 9.

11.

Relative impact of multi-layered genomic data on gene expression phenotypes in serous ovarian tumors.

Sohn KA, Kim D, Lim J, Kim JH.

BMC Syst Biol. 2013 Dec 13;7 Suppl 6:S9. doi: 10.1186/1752-0509-7-S6-S9.

12.

Identification of ovarian cancer driver genes by using module network integration of multi-omics data.

Gevaert O, Villalobos V, Sikic BI, Plevritis SK.

Interface Focus. 2013 Aug 6;3(4):20130013. doi: 10.1098/rsfs.2013.0013. Erratum in: Interface Focus. 2014 Jun 6;4(3):20140023.

13.

Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration.

Verbeke LP, Van den Eynden J, Fierro AC, Demeester P, Fostier J, Marchal K.

PLoS One. 2015 Jul 28;10(7):e0133503. doi: 10.1371/journal.pone.0133503. eCollection 2015.

14.
15.

Pathway-based classification of cancer subtypes.

Kim S, Kon M, DeLisi C.

Biol Direct. 2012 Jul 3;7:21. doi: 10.1186/1745-6150-7-21.

16.

Ovarian cancer: markers of response.

Na YJ, Farley J, Zeh A, del Carmen M, Penson R, Birrer MJ.

Int J Gynecol Cancer. 2009 Dec;19 Suppl 2:S21-9. doi: 10.1111/IGC.0b013e3181c2aeb5. Review.

PMID:
19955910
17.

An integrated genomic-based approach to individualized treatment of patients with advanced-stage ovarian cancer.

Dressman HK, Berchuck A, Chan G, Zhai J, Bild A, Sayer R, Cragun J, Clarke J, Whitaker RS, Li L, Gray J, Marks J, Ginsburg GS, Potti A, West M, Nevins JR, Lancaster JM.

J Clin Oncol. 2007 Feb 10;25(5):517-25. Retraction in: J Clin Oncol. 2012 Feb 20;30(6):678.

PMID:
17290060
18.

A Systemic Analysis of Transcriptomic and Epigenomic Data To Reveal Regulation Patterns for Complex Disease.

Xu C, Zhang JG, Lin D, Zhang L, Shen H, Deng HW.

G3 (Bethesda). 2017 Jul 5;7(7):2271-2279. doi: 10.1534/g3.117.042408.

19.

Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data.

El-Manzalawy Y, Hsieh TY, Shivakumar M, Kim D, Honavar V.

BMC Med Genomics. 2018 Sep 14;11(Suppl 3):71. doi: 10.1186/s12920-018-0388-0.

20.

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