124 related articles for article (PubMed ID: 25708115)
1. Investigating the utility of clinical outcome-guided mutual information network in network-based Cox regression.
Jeong HH; Kim S; Wee K; Sohn KA
BMC Syst Biol; 2015; 9 Suppl 1(Suppl 1):S8. PubMed ID: 25708115
[TBL] [Abstract][Full Text] [Related]
2. 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. PubMed ID: 23555212
[TBL] [Abstract][Full Text] [Related]
3. Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer.
Xu H; Moni MA; Liò P
Comput Biol Chem; 2015 Dec; 59 Pt B():15-31. PubMed ID: 26611766
[TBL] [Abstract][Full Text] [Related]
4. 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; 8():42. PubMed ID: 26138921
[TBL] [Abstract][Full Text] [Related]
5. 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. PubMed ID: 26048077
[TBL] [Abstract][Full Text] [Related]
6. DegreeCox - a network-based regularization method for survival analysis.
Veríssimo A; Oliveira AL; Sagot MF; Vinga S
BMC Bioinformatics; 2016 Dec; 17(Suppl 16):449. PubMed ID: 28105908
[TBL] [Abstract][Full Text] [Related]
7. A Five-Gene Expression Signature Predicts Clinical Outcome of Ovarian Serous Cystadenocarcinoma.
Liu LW; Zhang Q; Guo W; Qian K; Wang Q
Biomed Res Int; 2016; 2016():6945304. PubMed ID: 27478834
[TBL] [Abstract][Full Text] [Related]
8. Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data.
Gui J; Li H
Bioinformatics; 2005 Jul; 21(13):3001-8. PubMed ID: 15814556
[TBL] [Abstract][Full Text] [Related]
9. Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data.
Hao J; Kim Y; Mallavarapu T; Oh JH; Kang M
BMC Med Genomics; 2019 Dec; 12(Suppl 10):189. PubMed ID: 31865908
[TBL] [Abstract][Full Text] [Related]
10. Annexin A2 and S100A10 are independent predictors of serous ovarian cancer outcome.
Lokman NA; Pyragius CE; Ruszkiewicz A; Oehler MK; Ricciardelli C
Transl Res; 2016 May; 171():83-95.e1-2. PubMed ID: 26925708
[TBL] [Abstract][Full Text] [Related]
11. Cancer Markers Selection Using Network-Based Cox Regression: A Methodological and Computational Practice.
Iuliano A; Occhipinti A; Angelini C; De Feis I; Lió P
Front Physiol; 2016; 7():208. PubMed ID: 27378931
[TBL] [Abstract][Full Text] [Related]
12. Network-based survival-associated module biomarker and its crosstalk with cell death genes in ovarian cancer.
Jin N; Wu H; Miao Z; Huang Y; Hu Y; Bi X; Wu D; Qian K; Wang L; Wang C; Wang H; Li K; Li X; Wang D
Sci Rep; 2015 Jun; 5():11566. PubMed ID: 26099452
[TBL] [Abstract][Full Text] [Related]
13. The L
Jiang HK; Liang Y
Comput Biol Med; 2018 Sep; 100():203-208. PubMed ID: 30032047
[TBL] [Abstract][Full Text] [Related]
14. Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization.
Liang Y; Chai H; Liu XY; Xu ZB; Zhang H; Leung KS
BMC Med Genomics; 2016 Mar; 9():11. PubMed ID: 26932592
[TBL] [Abstract][Full Text] [Related]
15. Partial Cox regression analysis for high-dimensional microarray gene expression data.
Li H; Gui J
Bioinformatics; 2004 Aug; 20 Suppl 1():i208-15. PubMed ID: 15262801
[TBL] [Abstract][Full Text] [Related]
16. Molecular pathway identification using biological network-regularized logistic models.
Zhang W; Wan YW; Allen GI; Pang K; Anderson ML; Liu Z
BMC Genomics; 2013; 14 Suppl 8(Suppl 8):S7. PubMed ID: 24564637
[TBL] [Abstract][Full Text] [Related]
17. 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; 10(7):e0133503. PubMed ID: 26217958
[TBL] [Abstract][Full Text] [Related]
18. Prognostic significance of differential expression of angiogenic genes in women with high-grade serous ovarian carcinoma.
Siamakpour-Reihani S; Owzar K; Jiang C; Turner T; Deng Y; Bean SM; Horton JK; Berchuck A; Marks JR; Dewhirst MW; Alvarez Secord A
Gynecol Oncol; 2015 Oct; 139(1):23-9. PubMed ID: 26260910
[TBL] [Abstract][Full Text] [Related]
19. Network-based biomarkers enhance classical approaches to prognostic gene expression signatures.
Barter RL; Schramm SJ; Mann GJ; Yang YH
BMC Syst Biol; 2014; 8 Suppl 4(Suppl 4):S5. PubMed ID: 25521200
[TBL] [Abstract][Full Text] [Related]
20. Regression-Based Network Estimation for High-Dimensional Genetic Data.
Lee KM; Lee M; Seok J; Han SW
J Comput Biol; 2019 Apr; 26(4):336-349. PubMed ID: 30653343
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
