173 related articles for article (PubMed ID: 27378931)
1. 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]
2. Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice.
Simidjievski N; Bodnar C; Tariq I; Scherer P; Andres Terre H; Shams Z; Jamnik M; Liò P
Front Genet; 2019; 10():1205. PubMed ID: 31921281
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. 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]
6. Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.
Liu C; Wang X; Genchev GZ; Lu H
Methods; 2017 Jul; 124():100-107. PubMed ID: 28627406
[TBL] [Abstract][Full Text] [Related]
7. GSNFS: Gene subnetwork biomarker identification of lung cancer expression data.
Doungpan N; Engchuan W; Chan JH; Meechai A
BMC Med Genomics; 2016 Dec; 9(Suppl 3):70. PubMed ID: 28117655
[TBL] [Abstract][Full Text] [Related]
8. Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods.
Iuliano A; Occhipinti A; Angelini C; De Feis I; Liò P
Front Genet; 2018; 9():206. PubMed ID: 29963073
[TBL] [Abstract][Full Text] [Related]
9. Sparse overlapping group lasso for integrative multi-omics analysis.
Park H; Niida A; Miyano S; Imoto S
J Comput Biol; 2015 Feb; 22(2):73-84. PubMed ID: 25629319
[TBL] [Abstract][Full Text] [Related]
10. Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO.
Zuo Y; Cui Y; Yu G; Li R; Ressom HW
BMC Bioinformatics; 2017 Feb; 18(1):99. PubMed ID: 28187708
[TBL] [Abstract][Full Text] [Related]
11. A plea for taking all available clinical information into account when assessing the predictive value of omics data.
Volkmann A; De Bin R; Sauerbrei W; Boulesteix AL
BMC Med Res Methodol; 2019 Jul; 19(1):162. PubMed ID: 31340753
[TBL] [Abstract][Full Text] [Related]
12. EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma.
Shao B; Bjaanæs MM; Helland Å; Schütte C; Conrad T
PLoS One; 2019; 14(1):e0204186. PubMed ID: 30703089
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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; 11(Suppl 3):71. PubMed ID: 30255801
[TBL] [Abstract][Full Text] [Related]
15. BMRF-MI: integrative identification of protein interaction network by modeling the gene dependency.
Shi X; Wang X; Shajahan A; Hilakivi-Clarke L; Clarke R; Xuan J
BMC Genomics; 2015; 16 Suppl 7(Suppl 7):S10. PubMed ID: 26099273
[TBL] [Abstract][Full Text] [Related]
16. NETWORK-REGULARIZED HIGH-DIMENSIONAL COX REGRESSION FOR ANALYSIS OF GENOMIC DATA.
Sun H; Lin W; Feng R; Li H
Stat Sin; 2014 Jul; 24(3):1433-1459. PubMed ID: 26316678
[TBL] [Abstract][Full Text] [Related]
17. Prediction and interpretation of cancer survival using graph convolution neural networks.
Ramirez R; Chiu YC; Zhang S; Ramirez J; Chen Y; Huang Y; Jin YF
Methods; 2021 Aug; 192():120-130. PubMed ID: 33484826
[TBL] [Abstract][Full Text] [Related]
18. An Integrated Model Based on a Six-Gene Signature Predicts Overall Survival in Patients With Hepatocellular Carcinoma.
Li W; Lu J; Ma Z; Zhao J; Liu J
Front Genet; 2019; 10():1323. PubMed ID: 32010188
[No Abstract] [Full Text] [Related]
19. Combinatorial Ranking of Gene Sets to Predict Disease Relapse: The Retinoic Acid Pathway in Early Prostate Cancer.
Nim HT; Furtado MB; Ramialison M; Boyd SE
Front Oncol; 2017; 7():30. PubMed ID: 28361034
[TBL] [Abstract][Full Text] [Related]
20. Integration of multi-omics data to mine cancer-related gene modules.
Li P; Guo M; Sun B
J Bioinform Comput Biol; 2019 Dec; 17(6):1950038. PubMed ID: 32019413
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]