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Items: 1 to 20 of 79

1.

Improving breast cancer survival analysis through competition-based multidimensional modeling.

Bilal E, Dutkowski J, Guinney J, Jang IS, Logsdon BA, Pandey G, Sauerwine BA, Shimoni Y, Moen Vollan HK, Mecham BH, Rueda OM, Tost J, Curtis C, Alvarez MJ, Kristensen VN, Aparicio S, Børresen-Dale AL, Caldas C, Califano A, Friend SH, Ideker T, Schadt EE, Stolovitzky GA, Margolin AA.

PLoS Comput Biol. 2013;9(5):e1003047. doi: 10.1371/journal.pcbi.1003047. Epub 2013 May 9.

2.

Mixture classification model based on clinical markers for breast cancer prognosis.

Zeng T, Liu J.

Artif Intell Med. 2010 Feb-Mar;48(2-3):129-37. doi: 10.1016/j.artmed.2009.07.008. Epub 2009 Dec 14.

PMID:
20005686
3.

NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data.

Zou M, Liu Z, Zhang XS, Wang Y.

Bioinformatics. 2015 Oct 15;31(20):3330-8. doi: 10.1093/bioinformatics/btv374. Epub 2015 Jun 18.

PMID:
26092859
4.

An ensemble machine learning approach to predict survival in breast cancer.

Djebbari A, Liu Z, Phan S, Famili F.

Int J Comput Biol Drug Des. 2008;1(3):275-94.

PMID:
20054993
5.

A hybrid approach to survival model building using integration of clinical and molecular information in censored data.

Choi I, Kattan MW, Wells BJ, Yu C.

IEEE/ACM Trans Comput Biol Bioinform. 2012 Jul-Aug;9(4):1091-1105. doi: 10.1109/TCBB.2012.31.

PMID:
22350208
6.

Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer.

Margolin AA, Bilal E, Huang E, Norman TC, Ottestad L, Mecham BH, Sauerwine B, Kellen MR, Mangravite LM, Furia MD, Vollan HK, Rueda OM, Guinney J, Deflaux NA, Hoff B, Schildwachter X, Russnes HG, Park D, Vang VO, Pirtle T, Youseff L, Citro C, Curtis C, Kristensen VN, Hellerstein J, Friend SH, Stolovitzky G, Aparicio S, Caldas C, Børresen-Dale AL.

Sci Transl Med. 2013 Apr 17;5(181):181re1. doi: 10.1126/scitranslmed.3006112.

7.

Can survival prediction be improved by merging gene expression data sets?

Yasrebi H, Sperisen P, Praz V, Bucher P.

PLoS One. 2009 Oct 23;4(10):e7431. doi: 10.1371/journal.pone.0007431.

8.

Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets.

Martin KJ, Patrick DR, Bissell MJ, Fournier MV.

PLoS One. 2008 Aug 20;3(8):e2994. doi: 10.1371/journal.pone.0002994.

9.

Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures.

Fan C, Prat A, Parker JS, Liu Y, Carey LA, Troester MA, Perou CM.

BMC Med Genomics. 2011 Jan 9;4:3. doi: 10.1186/1755-8794-4-3.

10.

A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancer.

Huang S, Yee C, Ching T, Yu H, Garmire LX.

PLoS Comput Biol. 2014 Sep 18;10(9):e1003851. doi: 10.1371/journal.pcbi.1003851. eCollection 2014 Sep.

11.

Comparative survival analysis of breast cancer microarray studies identifies important prognostic genetic pathways.

Miecznikowski JC, Wang D, Liu S, Sucheston L, Gold D.

BMC Cancer. 2010 Oct 21;10:573. doi: 10.1186/1471-2407-10-573.

12.

Modeling precision treatment of breast cancer.

Daemen A, Griffith OL, Heiser LM, Wang NJ, Enache OM, Sanborn Z, Pepin F, Durinck S, Korkola JE, Griffith M, Hur JS, Huh N, Chung J, Cope L, Fackler MJ, Umbricht C, Sukumar S, Seth P, Sukhatme VP, Jakkula LR, Lu Y, Mills GB, Cho RJ, Collisson EA, van't Veer LJ, Spellman PT, Gray JW.

Genome Biol. 2013;14(10):R110. Erratum in: Genome Biol. 2015;16:95.

13.

NETBAGs: a network-based clustering approach with gene signatures for cancer subtyping analysis.

Wu L, Liu Z, Xu J, Chen M, Fang H, Tong W, Xiao W.

Biomark Med. 2015;9(11):1053-65. doi: 10.2217/bmm.15.96. Epub 2015 Oct 26.

14.

A fuzzy gene expression-based computational approach improves breast cancer prognostication.

Haibe-Kains B, Desmedt C, Rothé F, Piccart M, Sotiriou C, Bontempi G.

Genome Biol. 2010;11(2):R18. doi: 10.1186/gb-2010-11-2-r18. Epub 2010 Feb 15.

15.

Gene expression analysis in clear cell renal cell carcinoma using gene set enrichment analysis for biostatistical management.

Maruschke M, Reuter D, Koczan D, Hakenberg OW, Thiesen HJ.

BJU Int. 2011 Jul;108(2 Pt 2):E29-35. doi: 10.1111/j.1464-410X.2010.09794.x. Epub 2011 Mar 16.

16.

Robust and efficient identification of biomarkers by classifying features on graphs.

Hwang T, Sicotte H, Tian Z, Wu B, Kocher JP, Wigle DA, Kumar V, Kuang R.

Bioinformatics. 2008 Sep 15;24(18):2023-9. doi: 10.1093/bioinformatics/btn383. Epub 2008 Jul 24.

PMID:
18653521
17.

CoINcIDE: A framework for discovery of patient subtypes across multiple datasets.

Planey CR, Gevaert O.

Genome Med. 2016 Mar 9;8(1):27. doi: 10.1186/s13073-016-0281-4.

18.

A prognostic model for lymph node-negative breast cancer patients based on the integration of proliferation and immunity.

Oh E, Choi YL, Park T, Lee S, Nam SJ, Shin YK.

Breast Cancer Res Treat. 2012 Apr;132(2):499-509. doi: 10.1007/s10549-011-1626-8. Epub 2011 Jun 11.

PMID:
21667120
19.

Clinical relevance of DNA microarray analyses using archival formalin-fixed paraffin-embedded breast cancer specimens.

Sadi AM, Wang DY, Youngson BJ, Miller N, Boerner S, Done SJ, Leong WL.

BMC Cancer. 2011 Jun 16;11:253:1-13. doi: 10.1186/1471-2407-11-25.

20.

Health-related quality of life in early breast cancer.

Groenvold M.

Dan Med Bull. 2010 Sep;57(9):B4184.

PMID:
20816024

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