225 related articles for article (PubMed ID: 22558100)
1. A critical evaluation of network and pathway-based classifiers for outcome prediction in breast cancer.
Staiger C; Cadot S; Kooter R; Dittrich M; Müller T; Klau GW; Wessels LF
PLoS One; 2012; 7(4):e34796. PubMed ID: 22558100
[TBL] [Abstract][Full Text] [Related]
2. Comparative evaluation of network features for the prediction of breast cancer metastasis.
Adnan N; Liu Z; Huang THM; Ruan J
BMC Med Genomics; 2020 Apr; 13(Suppl 5):40. PubMed ID: 32241278
[TBL] [Abstract][Full Text] [Related]
3. Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis.
Staiger C; Cadot S; Györffy B; Wessels LF; Klau GW
Front Genet; 2013; 4():289. PubMed ID: 24391662
[TBL] [Abstract][Full Text] [Related]
4. Mixture classification model based on clinical markers for breast cancer prognosis.
Zeng T; Liu J
Artif Intell Med; 2010; 48(2-3):129-37. PubMed ID: 20005686
[TBL] [Abstract][Full Text] [Related]
5. An ensemble predictive modeling framework for breast cancer classification.
Nagarajan R; Upreti M
Methods; 2017 Dec; 131():128-134. PubMed ID: 28716511
[TBL] [Abstract][Full Text] [Related]
6. Analysis of the microarray gene expression for breast cancer progression after the application modified logistic regression.
Morais-Rodrigues F; Silv Erio-Machado R; Kato RB; Rodrigues DLN; Valdez-Baez J; Fonseca V; San EJ; Gomes LGR; Dos Santos RG; Vinicius Canário Viana M; da Cruz Ferraz Dutra J; Teixeira Dornelles Parise M; Parise D; Campos FF; de Souza SJ; Ortega JM; Barh D; Ghosh P; Azevedo VAC; Dos Santos MA
Gene; 2020 Feb; 726():144168. PubMed ID: 31759986
[TBL] [Abstract][Full Text] [Related]
7. Identification of a 6-gene signature for the survival prediction of breast cancer patients based on integrated multi-omics data analysis.
Mo W; Ding Y; Zhao S; Zou D; Ding X
PLoS One; 2020; 15(11):e0241924. PubMed ID: 33170908
[TBL] [Abstract][Full Text] [Related]
8. Identifying cancer biomarkers by network-constrained support vector machines.
Chen L; Xuan J; Riggins RB; Clarke R; Wang Y
BMC Syst Biol; 2011 Oct; 5():161. PubMed ID: 21992556
[TBL] [Abstract][Full Text] [Related]
9. The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set.
Milioli HH; Vimieiro R; Riveros C; Tishchenko I; Berretta R; Moscato P
PLoS One; 2015; 10(7):e0129711. PubMed ID: 26132585
[TBL] [Abstract][Full Text] [Related]
10. Prediction of breast cancer metastasis by gene expression profiles: a comparison of metagenes and single genes.
Burton M; Thomassen M; Tan Q; Kruse TA
Cancer Inform; 2012; 11():193-217. PubMed ID: 23304070
[TBL] [Abstract][Full Text] [Related]
11. A three-gene model to robustly identify breast cancer molecular subtypes.
Haibe-Kains B; Desmedt C; Loi S; Culhane AC; Bontempi G; Quackenbush J; Sotiriou C
J Natl Cancer Inst; 2012 Feb; 104(4):311-25. PubMed ID: 22262870
[TBL] [Abstract][Full Text] [Related]
12. Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context.
Abraham G; Kowalczyk A; Loi S; Haviv I; Zobel J
BMC Bioinformatics; 2010 May; 11():277. PubMed ID: 20500821
[TBL] [Abstract][Full Text] [Related]
13. A Novel Computational Approach for Biomarker Detection for Gene Expression-Based Computer-Aided Diagnostic Systems for Breast Cancer.
Al-Yousef A; Samarasinghe S
Methods Mol Biol; 2021; 2190():195-208. PubMed ID: 32804367
[TBL] [Abstract][Full Text] [Related]
14. Screening of Prognostic Factors in Early-Onset Breast Cancer.
Yu Z; He Q; Xu G
Technol Cancer Res Treat; 2020; 19():1533033819893670. PubMed ID: 32028860
[TBL] [Abstract][Full Text] [Related]
15. Stable feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation for biomarker discovery in breast cancer.
Chereda H; Leha A; Beißbarth T
Artif Intell Med; 2024 May; 151():102840. PubMed ID: 38658129
[TBL] [Abstract][Full Text] [Related]
16. A data-driven interactome of synergistic genes improves network-based cancer outcome prediction.
Allahyar A; Ubels J; de Ridder J
PLoS Comput Biol; 2019 Feb; 15(2):e1006657. PubMed ID: 30726216
[TBL] [Abstract][Full Text] [Related]
17. Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer.
Karlsson E; Delle U; Danielsson A; Olsson B; Abel F; Karlsson P; Helou K
BMC Cancer; 2008 Sep; 8():254. PubMed ID: 18778486
[TBL] [Abstract][Full Text] [Related]
18. Integration of clinical and gene expression data has a synergetic effect on predicting breast cancer outcome.
van Vliet MH; Horlings HM; van de Vijver MJ; Reinders MJ; Wessels LF
PLoS One; 2012; 7(7):e40358. PubMed ID: 22808140
[TBL] [Abstract][Full Text] [Related]
19. Development and validation of a fourteen- innate immunity-related gene pairs signature for predicting prognosis head and neck squamous cell carcinoma.
Zhang F; Liu Y; Yang Y; Yang K
BMC Cancer; 2020 Oct; 20(1):1015. PubMed ID: 33081731
[TBL] [Abstract][Full Text] [Related]
20. Incorporating topological information for predicting robust cancer subnetwork markers in human protein-protein interaction network.
Khunlertgit N; Yoon BJ
BMC Bioinformatics; 2016 Oct; 17(Suppl 13):351. PubMed ID: 27766944
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
